2018
Mikołajczyk, T; Nowicki, K; Bustillo, A; Pimenov, Yu D
Predicting tool life in turning operations using neural networks and image processing Journal Article
In: Mechanical Systems and Signal Processing, vol. 104, pp. 503 - 513, 2018, ISSN: 0888-3270.
Abstract | Links | BibTeX | Tags: Image analysis, Neural networks, Tool life prediction, Tool wear
@article{MIKOLAJCZYK2018503,
title = {Predicting tool life in turning operations using neural networks and image processing},
author = {T Mikołajczyk and K Nowicki and A Bustillo and Yu D Pimenov},
url = {http://www.sciencedirect.com/science/article/pii/S088832701730599X},
doi = {https://doi.org/10.1016/j.ymssp.2017.11.022},
issn = {0888-3270},
year = {2018},
date = {2018-05-01},
journal = {Mechanical Systems and Signal Processing},
volume = {104},
pages = {503 - 513},
abstract = {A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.},
keywords = {Image analysis, Neural networks, Tool life prediction, Tool wear},
pubstate = {published},
tppubtype = {article}
}
A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.
Kuncheva, Ludmila I; Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Gunn, Iain A D
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification Journal Article
In: arXiv, 2018.
Abstract | Links | BibTeX | Tags: Ensemble methods, geometric mean (GM), Imbalanced data, instance/prototype selection, nearest neighbour
@article{Kuncheva2018,
title = {Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification},
author = {Ludmila I Kuncheva and Álvar Arnaiz-González and José Francisco Díez-Pastor and Iain A D Gunn},
url = {https://arxiv.org/abs/1804.07155},
doi = {arXiv:1804.07155v1},
year = {2018},
date = {2018-04-19},
journal = {arXiv},
abstract = {A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.},
keywords = {Ensemble methods, geometric mean (GM), Imbalanced data, instance/prototype selection, nearest neighbour},
pubstate = {published},
tppubtype = {article}
}
A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.
Gunn, Iain A D; Arnaiz-González, Álvar; Kuncheva, Ludmila I
A taxonomic look at instance-based stream classifiers Journal Article
In: Neurocomputing, vol. 286, pp. 167-178, 2018, ISSN: 0925-2312.
Abstract | Links | BibTeX | Tags: Concept drift, Instance selection, Machine learning, Prototype generation, Stream classification
@article{Gunn2018,
title = {A taxonomic look at instance-based stream classifiers},
author = {Iain A D Gunn and Álvar Arnaiz-González and Ludmila I Kuncheva},
url = {https://www.sciencedirect.com/science/article/pii/S092523121830095X},
doi = {10.1016/j.neucom.2018.01.062},
issn = {0925-2312},
year = {2018},
date = {2018-04-19},
journal = {Neurocomputing},
volume = {286},
pages = {167-178},
abstract = {Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.},
keywords = {Concept drift, Instance selection, Machine learning, Prototype generation, Stream classification},
pubstate = {published},
tppubtype = {article}
}
Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.
Grzenda, Maciej; Bustillo, Andres
Semi-supervised roughness prediction with partly unlabeled vibration data streams Journal Article
In: Journal of Intelligent Manufacturing, pp. 1-13, 2018, ISSN: 1572-8145.
Abstract | Links | BibTeX | Tags: Face milling, Roughness prediction, Semi-supervised techniques, Unlabeled data
@article{Grzenda2018,
title = {Semi-supervised roughness prediction with partly unlabeled vibration data streams},
author = {Maciej Grzenda and Andres Bustillo},
url = {https://doi.org/10.1007/s10845-018-1413-z},
doi = {10.1007/s10845-018-1413-z},
issn = {1572-8145},
year = {2018},
date = {2018-03-23},
journal = {Journal of Intelligent Manufacturing},
pages = {1-13},
abstract = {Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.},
keywords = {Face milling, Roughness prediction, Semi-supervised techniques, Unlabeled data},
pubstate = {published},
tppubtype = {article}
}
Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.
Santos, Pedro; Maudes-Raedo, Jesús; Bustillo, Andrés
Identifying maximum imbalance in datasets for fault diagnosis of gearboxes Journal Article
In: Journal of Intelligent Manufacturing, vol. 29, no. 2, pp. 333-351, 2018, ISSN: 0956-5515.
Abstract | Links | BibTeX | Tags: Fault diagnosis, Metrics Gearbox, Multi-class imbalance, Wind turbines Ensembles
@article{Santos2018,
title = {Identifying maximum imbalance in datasets for fault diagnosis of gearboxes},
author = {Pedro Santos and Jesús Maudes-Raedo and Andrés Bustillo},
url = {https://link.springer.com/article/10.1007%2Fs10845-015-1110-0},
doi = {10.1007/s10845-015-1110-0},
issn = {0956-5515},
year = {2018},
date = {2018-02-01},
journal = {Journal of Intelligent Manufacturing},
volume = {29},
number = {2},
pages = {333-351},
abstract = {Research into fault diagnosis in rotating machinery with a wide range of variable loads and speeds, such as the gearboxes of wind turbines, is of great industrial interest. Although appropriate sensors have been identified, an intelligent system that classifies machine states remains an open issue, due to a paucity of datasets with sufficient fault cases. Many of the proposed solutions have been tested on balanced datasets, containing roughly equal percentages of wind-turbine failure instances and instances of correct performance. In practice, however, it is not possible to obtain balanced datasets under real operating conditions. Our objective is to identify the most suitable classification technique that will depend least of all on the level of imbalance in the dataset. We start by analysing different metrics for the comparison of classification techniques on imbalanced datasets. Our results pointed to the Unweighted Macro Average of the F-measure, which we consider the most suitable metric for this diagnosis. Then, an extensive set of classification techniques was tested on datasets with varying levels of imbalance. Our conclusion is that a Rotation Forest ensemble of C4.4 decision trees, modifying the training phase of the classifier with a cost-sensitive approach, is the most suitable prediction model for this industrial task. It maintained its good performance even when the minority classes rate was as low as 6.5 %, while the majority of the other classifiers were more sensitive to the level of database imbalance and failed standard performance objectives, when the minority classes rate was lower than 10.5 %.},
keywords = {Fault diagnosis, Metrics Gearbox, Multi-class imbalance, Wind turbines Ensembles},
pubstate = {published},
tppubtype = {article}
}
Research into fault diagnosis in rotating machinery with a wide range of variable loads and speeds, such as the gearboxes of wind turbines, is of great industrial interest. Although appropriate sensors have been identified, an intelligent system that classifies machine states remains an open issue, due to a paucity of datasets with sufficient fault cases. Many of the proposed solutions have been tested on balanced datasets, containing roughly equal percentages of wind-turbine failure instances and instances of correct performance. In practice, however, it is not possible to obtain balanced datasets under real operating conditions. Our objective is to identify the most suitable classification technique that will depend least of all on the level of imbalance in the dataset. We start by analysing different metrics for the comparison of classification techniques on imbalanced datasets. Our results pointed to the Unweighted Macro Average of the F-measure, which we consider the most suitable metric for this diagnosis. Then, an extensive set of classification techniques was tested on datasets with varying levels of imbalance. Our conclusion is that a Rotation Forest ensemble of C4.4 decision trees, modifying the training phase of the classifier with a cost-sensitive approach, is the most suitable prediction model for this industrial task. It maintained its good performance even when the minority classes rate was as low as 6.5 %, while the majority of the other classifiers were more sensitive to the level of database imbalance and failed standard performance objectives, when the minority classes rate was lower than 10.5 %.
Güemes-Peña, Diego; López-Nozal, Carlos; Marticorena-Sánchez, Raúl; Maudes-Raedo, Jesús
Emerging topics in mining software repositories Journal Article
In: Progress in Artificial Intelligence, pp. 1-11, 2018, ISSN: 2192-6360.
Abstract | Links | BibTeX | Tags: Data Mining, Machine learning, Software engineering, Software process, Software repository
@article{Güemes-Peña2018,
title = {Emerging topics in mining software repositories},
author = {Diego Güemes-Peña and Carlos López-Nozal and Raúl Marticorena-Sánchez and Jesús Maudes-Raedo},
url = {https://link.springer.com/content/pdf/10.1007/s13748-018-0147-7.pdf},
doi = {10.1007/s13748-018-0147-7},
issn = {2192-6360},
year = {2018},
date = {2018-01-01},
journal = {Progress in Artificial Intelligence},
pages = {1-11},
abstract = {A software process is a set of related activities that culminates in the production of a software package: specification, design, implementation, testing, evolution into new versions, and maintenance. There are also other supporting activities such as configuration and change management, quality assurance, project management, evaluation of user experience, etc. Software repositories are infrastructures to support all these activities. They can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This position paper on mining software repositories presents a review and a discussion of research in this field over the past decade. We also identify applied machine learning strategies, current working topics, and future challenges for the improvement of company decision-making systems. Machine learning is defined as the process of discovering patterns in data. It can be applied to software repositories, since every change is recorded as data. Companies can then use these patterns as the basis for their decision-making systems and for knowledge discovery.},
keywords = {Data Mining, Machine learning, Software engineering, Software process, Software repository},
pubstate = {published},
tppubtype = {article}
}
A software process is a set of related activities that culminates in the production of a software package: specification, design, implementation, testing, evolution into new versions, and maintenance. There are also other supporting activities such as configuration and change management, quality assurance, project management, evaluation of user experience, etc. Software repositories are infrastructures to support all these activities. They can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This position paper on mining software repositories presents a review and a discussion of research in this field over the past decade. We also identify applied machine learning strategies, current working topics, and future challenges for the improvement of company decision-making systems. Machine learning is defined as the process of discovering patterns in data. It can be applied to software repositories, since every change is recorded as data. Companies can then use these patterns as the basis for their decision-making systems and for knowledge discovery.
Mikolajczyk, Tadeusz; Fuwen, Hu; Moldovan, Liviu; Bustillo, Andres; Matuszewski, Maciej; Nowicki, Krzysztof
Selection of machining parameters with Android application made using MIT App Inventor bookmarks Journal Article
In: Procedia Manufacturing, vol. 22, pp. 172 - 179, 2018, ISSN: 2351-9789, (11th International Conference Interdisciplinarity in Engineering, INTER-ENG 2017, 5-6 October 2017, Tirgu Mures, Romania).
Abstract | Links | BibTeX | Tags: Android, machining parameters, MIT inventor, mobile application
@article{MIKOLAJCZYK2018172,
title = {Selection of machining parameters with Android application made using MIT App Inventor bookmarks},
author = {Tadeusz Mikolajczyk and Hu Fuwen and Liviu Moldovan and Andres Bustillo and Maciej Matuszewski and Krzysztof Nowicki},
url = {http://www.sciencedirect.com/science/article/pii/S2351978918303214},
doi = {https://doi.org/10.1016/j.promfg.2018.03.027},
issn = {2351-9789},
year = {2018},
date = {2018-01-01},
journal = {Procedia Manufacturing},
volume = {22},
pages = {172 - 179},
abstract = {Undoubtedly mobile devices are gaining more and more popularity. However, the breakthrough in mobile applications is yet to be followed by a breakthrough in manufacturing industry. The paper presents a new methodology for application development on the Android platform in MIT App Inventor bookmarks. The research method consists in an algorithm and application design. Also it was presented an example of an elaborate program SpeedCalc for the lathe spindle speed selection for the determined value of cutting speed in relation to the diameter of the work piece. The program can be used with a mobile phone or tablet.},
note = {11th International Conference Interdisciplinarity in Engineering, INTER-ENG 2017, 5-6 October 2017, Tirgu Mures, Romania},
keywords = {Android, machining parameters, MIT inventor, mobile application},
pubstate = {published},
tppubtype = {article}
}
Undoubtedly mobile devices are gaining more and more popularity. However, the breakthrough in mobile applications is yet to be followed by a breakthrough in manufacturing industry. The paper presents a new methodology for application development on the Android platform in MIT App Inventor bookmarks. The research method consists in an algorithm and application design. Also it was presented an example of an elaborate program SpeedCalc for the lathe spindle speed selection for the determined value of cutting speed in relation to the diameter of the work piece. The program can be used with a mobile phone or tablet.
2017
Maudes, Jesus; Bustillo, Andrés; Guerra, Antonio J; Ciurana, Joaquim
Random Forest ensemble prediction of stent dimensions in microfabrication processes Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 91, no. 1, pp. 879–893, 2017, ISSN: 1433-3015.
Abstract | Links | BibTeX | Tags: Data Mining, Ensembles of regressors, Random forest, Regression trees, Stents Laser machining
@article{Maudes2017,
title = {Random Forest ensemble prediction of stent dimensions in microfabrication processes},
author = {Jesus Maudes and Andrés Bustillo and Antonio J Guerra and Joaquim Ciurana},
url = {https://doi.org/10.1007/s00170-016-9695-9},
doi = {10.1007/s00170-016-9695-9},
issn = {1433-3015},
year = {2017},
date = {2017-07-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {91},
number = {1},
pages = {879–893},
abstract = {The recent development of new laser machine tools for the manufacture of micro-scale metallic components has boosted demand in the field of medical applications. However, the optimization of this process encounters a major problem: a knowledge gap concerning the relation between the controllable parameters of these machine tools and the quality of the machined components. Our research proposes a two-step strategy to approach this problem for the manufacture of stents. First, a screening test identifies good and bad performance conditions for the laser process and generates useful information on cutting performance; then, a stent is manufactured under different cutting conditions and the most accurate machine learning technique to model this process is identified. This strategy is validated with the performance of experiments that vary pulse duration, laser power, and cutting speed, and measure two geometrical characteristics of the stent geometry. The results showed that linear Support Vector Machines can identify good and bad cutting conditions, while Random Forest ensembles of regression trees can predict with high accuracy the two characteristics of the stent geometry under study. Besides, this technique can extract useful information from the screening test that improves its final accuracy. In view of the small dataset size, an alternative based on the leave-one-out technique was used, instead of standard cross validation, so as to assure the generalization capability of the models.},
keywords = {Data Mining, Ensembles of regressors, Random forest, Regression trees, Stents Laser machining},
pubstate = {published},
tppubtype = {article}
}
The recent development of new laser machine tools for the manufacture of micro-scale metallic components has boosted demand in the field of medical applications. However, the optimization of this process encounters a major problem: a knowledge gap concerning the relation between the controllable parameters of these machine tools and the quality of the machined components. Our research proposes a two-step strategy to approach this problem for the manufacture of stents. First, a screening test identifies good and bad performance conditions for the laser process and generates useful information on cutting performance; then, a stent is manufactured under different cutting conditions and the most accurate machine learning technique to model this process is identified. This strategy is validated with the performance of experiments that vary pulse duration, laser power, and cutting speed, and measure two geometrical characteristics of the stent geometry. The results showed that linear Support Vector Machines can identify good and bad cutting conditions, while Random Forest ensembles of regression trees can predict with high accuracy the two characteristics of the stent geometry under study. Besides, this technique can extract useful information from the screening test that improves its final accuracy. In view of the small dataset size, an alternative based on the leave-one-out technique was used, instead of standard cross validation, so as to assure the generalization capability of the models.
Kuncheva, Ludmila I; Rodríguez, Juan José; Jackson, Aaron S
Restricted set classification: Who is there? Journal Article
In: Pattern Recognition, vol. 63, pp. 158-170, 2017, ISSN: 0031-3203.
Abstract | Links | BibTeX | Tags: Chess pieces classification, Compound decision problem, Object classification, Pattern recognition, Restricted set classification, SELECTED
@article{Kuncheva2017,
title = {Restricted set classification: Who is there?},
author = {Ludmila I Kuncheva and Juan José Rodríguez and Aaron S Jackson},
url = {https://www.sciencedirect.com/science/article/pii/S0031320316302412},
doi = {10.1016/j.patcog.2016.08.028},
issn = {0031-3203},
year = {2017},
date = {2017-03-01},
journal = {Pattern Recognition},
volume = {63},
pages = {158-170},
abstract = {We consider a problem where a set X of N objects (instances) coming from c classes have to be classified simultaneously. A restriction is imposed on X in that the maximum possible number of objects from each class is known, hence we dubbed the problem who-is-there? We compare three approaches to this problem: (1) independent classification whereby each object is labelled in the class with the largest posterior probability; (2) a greedy approach which enforces the restriction; and (3) a theoretical approach which, in addition, maximises the likelihood of the label assignment, implemented through the Hungarian assignment algorithm. Our experimental study consists of two parts. The first part includes a custom-made chess data set where the pieces on the chess board must be recognised together from an image of the board. In the second part, we simulate the restricted set classification scenario using 96 datasets from a recently collated repository (University of Santiago de Compostela, USC). Our results show that the proposed approach (3) outperforms approaches (1) and (2).},
keywords = {Chess pieces classification, Compound decision problem, Object classification, Pattern recognition, Restricted set classification, SELECTED},
pubstate = {published},
tppubtype = {article}
}
We consider a problem where a set X of N objects (instances) coming from c classes have to be classified simultaneously. A restriction is imposed on X in that the maximum possible number of objects from each class is known, hence we dubbed the problem who-is-there? We compare three approaches to this problem: (1) independent classification whereby each object is labelled in the class with the largest posterior probability; (2) a greedy approach which enforces the restriction; and (3) a theoretical approach which, in addition, maximises the likelihood of the label assignment, implemented through the Hungarian assignment algorithm. Our experimental study consists of two parts. The first part includes a custom-made chess data set where the pieces on the chess board must be recognised together from an image of the board. In the second part, we simulate the restricted set classification scenario using 96 datasets from a recently collated repository (University of Santiago de Compostela, USC). Our results show that the proposed approach (3) outperforms approaches (1) and (2).
Sáiz-Manzanares, María Consuelo; Marticorena-Sánchez, Raúl; García-Osorio, César; Díez-Pastor, José Francisco
How Do B-Learning and Learning Patterns Influence Learning Outcomes? Journal Article
In: Frontiers in Psychology, vol. 8, pp. 745, 2017, ISSN: 1664-1078.
Abstract | Links | BibTeX | Tags: B-Learning, Computer Science teaching, eLearning, SELECTED
@article{10.3389/fpsyg.2017.00745,
title = {How Do B-Learning and Learning Patterns Influence Learning Outcomes?},
author = {María Consuelo Sáiz-Manzanares and Raúl Marticorena-Sánchez and César García-Osorio and José Francisco Díez-Pastor},
url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00745},
doi = {10.3389/fpsyg.2017.00745},
issn = {1664-1078},
year = {2017},
date = {2017-01-01},
journal = {Frontiers in Psychology},
volume = {8},
pages = {745},
abstract = {Learning Management System (LMS) platforms provide a wealth of information on the learning patterns of students. Learning Analytics (LA) techniques permit the analysis of the logs or records of the activities of both students and teachers on the on-line platform. The learning patterns differ depending on the type of Blended Learning (B-Learning). In this study, we analyse: 1) whether significant differences exist between the learning outcomes of students and their learning patterns on the platform, depending on the type of B-Learning [Replacement blend (RB) vs. Supplemental blend (SB)]; 2) whether a relation exists between the metacognitive and the motivational strategies of students, their learning outcomes and their learning patterns on the platform. The 87,065 log records of 129 students (69 in RB and 60 in SB) in the Moodle 3.1 platform were analysed. The results revealed different learning patterns between students depending on the type of B-Learning (RB vs. SB). We have found that the degree of blend, RB vs. SB, seems to condition student behaviour on the platform. Learning patterns in RB environments can predict student learning outcomes. Additionally, in RB environments there is a relationship between the learning patterns and the metacognitive and motivational strategies of the students.},
keywords = {B-Learning, Computer Science teaching, eLearning, SELECTED},
pubstate = {published},
tppubtype = {article}
}
Learning Management System (LMS) platforms provide a wealth of information on the learning patterns of students. Learning Analytics (LA) techniques permit the analysis of the logs or records of the activities of both students and teachers on the on-line platform. The learning patterns differ depending on the type of Blended Learning (B-Learning). In this study, we analyse: 1) whether significant differences exist between the learning outcomes of students and their learning patterns on the platform, depending on the type of B-Learning [Replacement blend (RB) vs. Supplemental blend (SB)]; 2) whether a relation exists between the metacognitive and the motivational strategies of students, their learning outcomes and their learning patterns on the platform. The 87,065 log records of 129 students (69 in RB and 60 in SB) in the Moodle 3.1 platform were analysed. The results revealed different learning patterns between students depending on the type of B-Learning (RB vs. SB). We have found that the degree of blend, RB vs. SB, seems to condition student behaviour on the platform. Learning patterns in RB environments can predict student learning outcomes. Additionally, in RB environments there is a relationship between the learning patterns and the metacognitive and motivational strategies of the students.
Rodríguez, Juan José; Quintana, Guillem; Bustillo, Andrés; Ciurana, Joaquim
A decision-making tool based on decision trees for roughness prediction in face milling Journal Article
In: International Journal of Computer Integrated Manufacturing, vol. 30, no. 9, 2017, ISSN: 0951-192X.
Abstract | Links | BibTeX | Tags: AI in manufacturing systems, cost management, decision support systems, Decision trees, process control, surface roughness, tool condition monitoring
@article{Rodríguez2017,
title = {A decision-making tool based on decision trees for roughness prediction in face milling},
author = {Juan José Rodríguez and Guillem Quintana and Andrés Bustillo and Joaquim Ciurana},
url = {https://www.tandfonline.com/doi/full/10.1080/0951192X.2016.1247991},
doi = {10.1080/0951192X.2016.1247991},
issn = {0951-192X},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Computer Integrated Manufacturing},
volume = {30},
number = {9},
abstract = {The selection of the right cutting tool in manufacturing process design is always an open question, especially when different tools are available on the market with similar characteristics, but marked differences in price, ranging from low-cost to high-performance cutting tools. The ultimate decision of the engineer will depend on previous experience with the life cycle of the tool and its performance, but without the support of a systematic knowledge base. This research presents a decision-making system based on soft-computing techniques. First, several experiments were carried out with four different cutting tools: two flat-milling low-cost tools without any surface treatment or coating and two high-performance, high-cost cutting tools (in both cases with four cutting edges, similar geometrical features and diameters). Three different measures of tool wear are considered in the context of real workshop conditions: on-line power consumption, cutting length and volume of cut material. Finally, decision trees have been selected as the most suitable technique for building a decision-making system for two reasons: these trees show higher accuracy for the prediction of roughness in terms of tool wear and tool type. They also provide useful visual feedback on the information that is extracted from the real data, which can be directly used by the process engineer.},
keywords = {AI in manufacturing systems, cost management, decision support systems, Decision trees, process control, surface roughness, tool condition monitoring},
pubstate = {published},
tppubtype = {article}
}
The selection of the right cutting tool in manufacturing process design is always an open question, especially when different tools are available on the market with similar characteristics, but marked differences in price, ranging from low-cost to high-performance cutting tools. The ultimate decision of the engineer will depend on previous experience with the life cycle of the tool and its performance, but without the support of a systematic knowledge base. This research presents a decision-making system based on soft-computing techniques. First, several experiments were carried out with four different cutting tools: two flat-milling low-cost tools without any surface treatment or coating and two high-performance, high-cost cutting tools (in both cases with four cutting edges, similar geometrical features and diameters). Three different measures of tool wear are considered in the context of real workshop conditions: on-line power consumption, cutting length and volume of cut material. Finally, decision trees have been selected as the most suitable technique for building a decision-making system for two reasons: these trees show higher accuracy for the prediction of roughness in terms of tool wear and tool type. They also provide useful visual feedback on the information that is extracted from the real data, which can be directly used by the process engineer.
2016
Palasciano, Claudio; Bustillo, Andres; Fantini, Paola; Taisch, Marco
A new approach for machine's management: from machine's signal acquisition to energy indexes Journal Article
In: Journal of Cleaner Production, vol. 137, pp. 1503 - 1515, 2016, ISSN: 0959-6526.
Abstract | Links | BibTeX | Tags: Energy and resource efficient manufacturing, Energy efficiency KPIs, Energy efficient manufacturing modeling, Energy-aware machine control
@article{PALASCIANO20161503,
title = {A new approach for machine's management: from machine's signal acquisition to energy indexes},
author = {Claudio Palasciano and Andres Bustillo and Paola Fantini and Marco Taisch},
url = {http://www.sciencedirect.com/science/article/pii/S0959652616309180},
doi = {https://doi.org/10.1016/j.jclepro.2016.07.030},
issn = {0959-6526},
year = {2016},
date = {2016-11-20},
journal = {Journal of Cleaner Production},
volume = {137},
pages = {1503 - 1515},
abstract = {In the highly competitive modern-day industrial landscape, characterized by globalization and resource scarcity, manufacturers are striving to improve economic and environmental performance. Innovation that enables self-adjustment, control and optimization of the energy consumption of individual machines continues. However, more research is needed if such systems are to be deployed successfully, especially considering the complex characteristics of the energy flows in the factory. In this paper we propose a novel approach to the coordination of information, processing and sensing systems for energy and resource efficient production systems. By leveraging on a recently-developed framework focusing on physical flows of energy, materials and waste we propose a solution based on specific energy efficiency KPIs and an online data acquisition/processing system, that enables real-time monitoring of the current status of the machining process and lagging assessment of system energy efficiency. The proposed solution allows the identification of abnormal energy consumption during the operational machine cycle, caused by incorrect part dimensioning or erroneous cutting conditions programmed by the process engineer, enabling identification of potential disruptions with different gravity levels, and delivery of meaningful alarms for the operator. Adaptive control of the machine cutting conditions or even trajectory re-programming is then possible, by correlating the energy-consumption data with other data, such as head temperature. Furthermore, by analysing the energy consumption of value and non value adding activities over complete production cycles (such as a shift or day), it is possible to monitor the progress of production systems toward achieving energy efficiency targets and to conduct root-cause analysis of inefficient energy usage for continuous improvement programs. We tested the proposed solution, modeling, index system ad online data acquisition/processing platform, through an industrial case study by deploying the developed hardware and software modules on a Nicolás Correa S.A. VERSA milling machine.},
keywords = {Energy and resource efficient manufacturing, Energy efficiency KPIs, Energy efficient manufacturing modeling, Energy-aware machine control},
pubstate = {published},
tppubtype = {article}
}
In the highly competitive modern-day industrial landscape, characterized by globalization and resource scarcity, manufacturers are striving to improve economic and environmental performance. Innovation that enables self-adjustment, control and optimization of the energy consumption of individual machines continues. However, more research is needed if such systems are to be deployed successfully, especially considering the complex characteristics of the energy flows in the factory. In this paper we propose a novel approach to the coordination of information, processing and sensing systems for energy and resource efficient production systems. By leveraging on a recently-developed framework focusing on physical flows of energy, materials and waste we propose a solution based on specific energy efficiency KPIs and an online data acquisition/processing system, that enables real-time monitoring of the current status of the machining process and lagging assessment of system energy efficiency. The proposed solution allows the identification of abnormal energy consumption during the operational machine cycle, caused by incorrect part dimensioning or erroneous cutting conditions programmed by the process engineer, enabling identification of potential disruptions with different gravity levels, and delivery of meaningful alarms for the operator. Adaptive control of the machine cutting conditions or even trajectory re-programming is then possible, by correlating the energy-consumption data with other data, such as head temperature. Furthermore, by analysing the energy consumption of value and non value adding activities over complete production cycles (such as a shift or day), it is possible to monitor the progress of production systems toward achieving energy efficiency targets and to conduct root-cause analysis of inefficient energy usage for continuous improvement programs. We tested the proposed solution, modeling, index system ad online data acquisition/processing platform, through an industrial case study by deploying the developed hardware and software modules on a Nicolás Correa S.A. VERSA milling machine.
Bustillo, Andres; Lacalle, Luis López N; Fernández-Valdivielso, Asier; Santos, Pedro
Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components Journal Article
In: Journal of Computational Design and Engineering, vol. 3, no. 4, pp. 337 - 348, 2016, ISSN: 2288-4300.
Abstract | Links | BibTeX | Tags: Ensembles, Forming taps, Regression trees, Roll taps, Roll-tap wear, Rotation forest, Threading
@article{BUSTILLO2016337,
title = {Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components},
author = {Andres Bustillo and Luis López N Lacalle and Asier Fernández-Valdivielso and Pedro Santos},
url = {http://www.sciencedirect.com/science/article/pii/S2288430016300306},
doi = {https://doi.org/10.1016/j.jcde.2016.06.002},
issn = {2288-4300},
year = {2016},
date = {2016-10-01},
journal = {Journal of Computational Design and Engineering},
volume = {3},
number = {4},
pages = {337 - 348},
abstract = {An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.},
keywords = {Ensembles, Forming taps, Regression trees, Roll taps, Roll-tap wear, Rotation forest, Threading},
pubstate = {published},
tppubtype = {article}
}
An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.
Arnaiz-González, Álvar; Blachnik, Marcin; Kordos, Mirosław; García-Osorio, César
Fusion of instance selection methods in regression tasks Journal Article
In: Information Fusion, vol. 30, pp. 69 - 79, 2016, ISSN: 1566-2535.
Abstract | Links | BibTeX | Tags: Data Mining, Ensemble methods, Instance selection, Regression, SELECTED
@article{ArnaizGonzalez201669,
title = {Fusion of instance selection methods in regression tasks},
author = {Álvar Arnaiz-González and Marcin Blachnik and Mirosław Kordos and César García-Osorio},
url = {http://www.sciencedirect.com/science/article/pii/S1566253515001141},
doi = {10.1016/j.inffus.2015.12.002},
issn = {1566-2535},
year = {2016},
date = {2016-01-01},
journal = {Information Fusion},
volume = {30},
pages = {69 - 79},
abstract = {Abstract Data pre-processing is a very important aspect of data mining. In this paper we discuss instance selection used for prediction algorithms, which is one of the pre-processing approaches. The purpose of instance selection is to improve the data quality by data size reduction and noise elimination. Until recently, instance selection has been applied mainly to classification problems. Very few recent papers address instance selection for regression tasks. This paper proposes fusion of instance selection algorithms for regression tasks to improve the selection performance. As the members of the ensemble two different families of instance selection methods are evaluated: one based on distance threshold and the other one on converting the regression task into a multiple class classification task. Extensive experimental evaluation performed on the two regression versions of the Edited Nearest Neighbor (ENN) and Condensed Nearest Neighbor (CNN) methods showed that the best performance measured by the error value and data size reduction are in most cases obtained for the ensemble methods.},
keywords = {Data Mining, Ensemble methods, Instance selection, Regression, SELECTED},
pubstate = {published},
tppubtype = {article}
}
Abstract Data pre-processing is a very important aspect of data mining. In this paper we discuss instance selection used for prediction algorithms, which is one of the pre-processing approaches. The purpose of instance selection is to improve the data quality by data size reduction and noise elimination. Until recently, instance selection has been applied mainly to classification problems. Very few recent papers address instance selection for regression tasks. This paper proposes fusion of instance selection algorithms for regression tasks to improve the selection performance. As the members of the ensemble two different families of instance selection methods are evaluated: one based on distance threshold and the other one on converting the regression task into a multiple class classification task. Extensive experimental evaluation performed on the two regression versions of the Edited Nearest Neighbor (ENN) and Condensed Nearest Neighbor (CNN) methods showed that the best performance measured by the error value and data size reduction are in most cases obtained for the ensemble methods.
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César
Instance selection for regression by discretization Journal Article
In: Expert Systems With Applications, 2016, ISSN: 0957-4174.
Links | BibTeX | Tags: Data Mining, Instance selection, Regression
@article{ArnaizGonzalez201669b,
title = {Instance selection for regression by discretization},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio},
doi = {10.1016/j.eswa.2015.12.046},
issn = {0957-4174},
year = {2016},
date = {2016-01-01},
journal = {Expert Systems With Applications},
keywords = {Data Mining, Instance selection, Regression},
pubstate = {published},
tppubtype = {article}
}
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César
Instance selection for regression: Adapting DROP Journal Article
In: Neurocomputing, vol. 201, pp. 66–81, 2016, ISSN: 0925-2312.
Abstract | Links | BibTeX | Tags: Data Mining, DROP, Instance selection, Noise filtering, Regression
@article{ArnaizGonzález2016,
title = {Instance selection for regression: Adapting DROP},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio},
url = {http://www.sciencedirect.com/science/article/pii/S0925231216301953},
doi = {10.1016/j.neucom.2016.04.003},
issn = {0925-2312},
year = {2016},
date = {2016-01-01},
journal = {Neurocomputing},
volume = {201},
pages = {66–81},
abstract = {Abstract Machine Learning has two central processes of interest that captivate the scientific community: classification and regression. Although instance selection for classification has shown its usefulness and has been researched in depth, instance selection for regression has not followed the same path and there are few published algorithms on the subject. In this paper, we propose that various adaptations of DROP, a well-known family of instance selection methods for classification, be applied to regression. Their behaviour is analysed using a broad range of datasets. The results are presented of the analysis of four new proposals for the reduction of dataset size, the effect on error when several classifiers are trained with the reduced dataset, and their robustness against noise. This last aspect is especially important, since in real life, it is frequent that the registered data be inexact and present distortions due to different causes: errors in the measurement tools, typos when writing results, existence of outliers and spurious readings, corruption in files, etc. When the datasets are small it is possible to manually correct these problems, but for big and huge datasets is better to have automatic methods to deal with these problems. In the experimental part, the proposed methods are found to be quite robust to noise.},
keywords = {Data Mining, DROP, Instance selection, Noise filtering, Regression},
pubstate = {published},
tppubtype = {article}
}
Abstract Machine Learning has two central processes of interest that captivate the scientific community: classification and regression. Although instance selection for classification has shown its usefulness and has been researched in depth, instance selection for regression has not followed the same path and there are few published algorithms on the subject. In this paper, we propose that various adaptations of DROP, a well-known family of instance selection methods for classification, be applied to regression. Their behaviour is analysed using a broad range of datasets. The results are presented of the analysis of four new proposals for the reduction of dataset size, the effect on error when several classifiers are trained with the reduced dataset, and their robustness against noise. This last aspect is especially important, since in real life, it is frequent that the registered data be inexact and present distortions due to different causes: errors in the measurement tools, typos when writing results, existence of outliers and spurious readings, corruption in files, etc. When the datasets are small it is possible to manually correct these problems, but for big and huge datasets is better to have automatic methods to deal with these problems. In the experimental part, the proposed methods are found to be quite robust to noise.
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César
Instance selection of linear complexity for big data Journal Article
In: Knowledge-Based Systems, vol. 107, pp. 83–95, 2016, ISSN: 0950-7051.
Abstract | Links | BibTeX | Tags: Big data, Data Mining, Data reduction, Hashing, Instance selection, Nearest neighbors, SELECTED
@article{ArnaizGonzálezLSHIS2016,
title = {Instance selection of linear complexity for big data},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio},
url = {http://www.sciencedirect.com/science/article/pii/S0950705116301617},
doi = {10.1016/j.knosys.2016.05.056},
issn = {0950-7051},
year = {2016},
date = {2016-01-01},
journal = {Knowledge-Based Systems},
volume = {107},
pages = {83–95},
abstract = {Abstract Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing to find similarities between instances. While the complexity of conventional methods (usually quadratic, O ( n 2 ) , or log-linear, O ( n log n ) ) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances).},
keywords = {Big data, Data Mining, Data reduction, Hashing, Instance selection, Nearest neighbors, SELECTED},
pubstate = {published},
tppubtype = {article}
}
Abstract Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing to find similarities between instances. While the complexity of conventional methods (usually quadratic, O ( n 2 ) , or log-linear, O ( n log n ) ) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances).
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
Random feature weights for regression trees Journal Article
In: Progress in Artificial Intelligence, vol. 5, no. 2, pp. 91–103, 2016, ISSN: 2192-6360.
Abstract | Links | BibTeX | Tags: Data Mining, Ensemble methods, Regression
@article{Arnaiz-González2016,
title = {Random feature weights for regression trees},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez},
url = {http://dx.doi.org/10.1007/s13748-016-0081-5},
doi = {10.1007/s13748-016-0081-5},
issn = {2192-6360},
year = {2016},
date = {2016-01-01},
journal = {Progress in Artificial Intelligence},
volume = {5},
number = {2},
pages = {91–103},
abstract = {Ensembles are learning methods the operation of which relies on a combination of different base models. The diversity of ensembles is a fundamental aspect that conditions their operation. Random Feature Weights RFW was proposed as a classification-tree ensemble construction method in which diversity is introduced into each tree by means of a random weight associated with each attribute. These weights vary from one tree to another in the ensemble. In this article, the idea of RFW is adapted to decision-tree regression. A comparison is drawn with other ensemble construction methods: Bagging, Random Forest, Iterated Bagging, Random Subspaces and AdaBoost.R2 obtaining competitive results.},
keywords = {Data Mining, Ensemble methods, Regression},
pubstate = {published},
tppubtype = {article}
}
Ensembles are learning methods the operation of which relies on a combination of different base models. The diversity of ensembles is a fundamental aspect that conditions their operation. Random Feature Weights RFW was proposed as a classification-tree ensemble construction method in which diversity is introduced into each tree by means of a random weight associated with each attribute. These weights vary from one tree to another in the ensemble. In this article, the idea of RFW is adapted to decision-tree regression. A comparison is drawn with other ensemble construction methods: Bagging, Random Forest, Iterated Bagging, Random Subspaces and AdaBoost.R2 obtaining competitive results.
2015
Santos, Pedro; Villa, Luisa F; Reñones, Anibal; Bustillo, Andrés; Maudes-Raedo, Jesús
An SVM-Based Solution for Fault Detection in Wind Turbines Journal Article
In: Sensors, vol. 15, no. 3, pp. 5627-5648, 2015, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Fault diagnosis, Neural networks, Support vector machines, wind turbines
@article{Santos2015,
title = {An SVM-Based Solution for Fault Detection in Wind Turbines},
author = {Pedro Santos and Luisa F Villa and Anibal Reñones and Andrés Bustillo and Jesús Maudes-Raedo},
url = {http://www.mdpi.com/1424-8220/15/3/5627},
doi = {10.3390/s150305627},
issn = {1424-8220},
year = {2015},
date = {2015-03-09},
journal = {Sensors},
volume = {15},
number = {3},
pages = {5627-5648},
abstract = {Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.},
keywords = {Fault diagnosis, Neural networks, Support vector machines, wind turbines},
pubstate = {published},
tppubtype = {article}
}
Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.
Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César; Kuncheva, Ludmila I
Random Balance: Ensembles of variable priors classifiers for imbalanced data Journal Article
In: Knowledge-Based Systems, vol. 85, pp. 96-111, 2015, ISSN: 0950-7051.
Abstract | Links | BibTeX | Tags: AdaBoost, Bagging, Class-imbalanced problems, Classifier ensembles, Data Mining, Ensemble methods, SELECTED, SMOTE, Undersampling
@article{RandomBalance,
title = {Random Balance: Ensembles of variable priors classifiers for imbalanced data},
author = {José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio and Ludmila I Kuncheva},
url = {http://www.sciencedirect.com/science/article/pii/S0950705115001720},
doi = {10.1016/j.knosys.2015.04.022},
issn = {0950-7051},
year = {2015},
date = {2015-01-01},
journal = {Knowledge-Based Systems},
volume = {85},
pages = {96-111},
abstract = {Abstract In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Class-imbalanced problems sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampled from the training set and augmented by artificial instances obtained using SMOTE. The novelty in the approach is that the proportions of the classes for each ensemble member are chosen randomly. The intuition behind the method is that the proposed diversity heuristic will ensure that the ensemble contains classifiers that are specialized for different operating points on the ROC space, thereby leading to larger AUC compared to other ensembles of classifiers. Experiments have been carried out to test the Random Balance approach by itself, and also in combination with standard ensemble methods. As a result, we propose a new ensemble creation method called RB-Boost which combines Random Balance with AdaBoost.M2. This combination involves enforcing random class proportions in addition to instance re-weighting. Experiments with 86 imbalanced data sets from two well known repositories demonstrate the advantage of the Random Balance approach.},
keywords = {AdaBoost, Bagging, Class-imbalanced problems, Classifier ensembles, Data Mining, Ensemble methods, SELECTED, SMOTE, Undersampling},
pubstate = {published},
tppubtype = {article}
}
Abstract In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Class-imbalanced problems sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampled from the training set and augmented by artificial instances obtained using SMOTE. The novelty in the approach is that the proportions of the classes for each ensemble member are chosen randomly. The intuition behind the method is that the proposed diversity heuristic will ensure that the ensemble contains classifiers that are specialized for different operating points on the ROC space, thereby leading to larger AUC compared to other ensembles of classifiers. Experiments have been carried out to test the Random Balance approach by itself, and also in combination with standard ensemble methods. As a result, we propose a new ensemble creation method called RB-Boost which combines Random Balance with AdaBoost.M2. This combination involves enforcing random class proportions in addition to instance re-weighting. Experiments with 86 imbalanced data sets from two well known repositories demonstrate the advantage of the Random Balance approach.
Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César; Kuncheva, Ludmila I
Diversity techniques improve the performance of the best imbalance learning ensembles Journal Article
In: Information Sciences, vol. 325, pp. 98 - 117, 2015, ISSN: 0020-0255.
Abstract | Links | BibTeX | Tags: Class-imbalanced problems, Classifier ensembles, Data Mining, Diversity, Ensemble methods, Rotation forest, SELECTED, SMOTE, Undersampling
@article{DiezPastor201598,
title = {Diversity techniques improve the performance of the best imbalance learning ensembles},
author = {José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio and Ludmila I Kuncheva},
url = {http://www.sciencedirect.com/science/article/pii/S0020025515005186},
doi = {10.1016/j.ins.2015.07.025},
issn = {0020-0255},
year = {2015},
date = {2015-01-01},
journal = {Information Sciences},
volume = {325},
pages = {98 - 117},
abstract = {Abstract Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques.},
keywords = {Class-imbalanced problems, Classifier ensembles, Data Mining, Diversity, Ensemble methods, Rotation forest, SELECTED, SMOTE, Undersampling},
pubstate = {published},
tppubtype = {article}
}
Abstract Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques.
López, Carlos; Marticorena-Sánchez, Raúl; Díez-Pastor, José Francisco; García-Osorio, César
Acquisition of Transferable Skills Associated with Software Maintenance and Development Using Tools for Versioning and Task Management Journal Article
In: International Journal of Engineering Education, vol. 31, no. 3, pp. 839–850, 2015, ISSN: 0949-149X.
Abstract | BibTeX | Tags: Computer Science teaching, Organizational skills, Software configuration, Software engineering education, Transferable skills
@article{clopezno2015,
title = {Acquisition of Transferable Skills Associated with Software Maintenance and Development Using Tools for Versioning and Task Management},
author = {Carlos López and Raúl Marticorena-Sánchez and José Francisco Díez-Pastor and César García-Osorio},
issn = {0949-149X},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Engineering Education},
volume = {31},
number = {3},
pages = {839–850},
abstract = {Tools for version control and task planning allow monitoring and collecting information on the software development and maintenance processes. This work describes the use of these types of tools in subject modules related to these fields. Instead of simply describing the tools as part of the subject content, the idea is to use them to promote and evaluate the acquisition of certain generic skills related to the subjects. After selecting the skills, this paper surveys the possible tools and their field of application at different levels of mastery, and concludes with an analysis of the impact of selected tools in the acquisition of those skills. This analysis was conducted through surveys of students from different courses in the knowledge area of software engineering.},
keywords = {Computer Science teaching, Organizational skills, Software configuration, Software engineering education, Transferable skills},
pubstate = {published},
tppubtype = {article}
}
Tools for version control and task planning allow monitoring and collecting information on the software development and maintenance processes. This work describes the use of these types of tools in subject modules related to these fields. Instead of simply describing the tools as part of the subject content, the idea is to use them to promote and evaluate the acquisition of certain generic skills related to the subjects. After selecting the skills, this paper surveys the possible tools and their field of application at different levels of mastery, and concludes with an analysis of the impact of selected tools in the acquisition of those skills. This analysis was conducted through surveys of students from different courses in the knowledge area of software engineering.
2014
Santos, Pedro; Teixidor, Daniel; Maudes-Raedo, Jesús; Ciurana, Joaquim
Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles Journal Article
In: Journal of Applied Mathematics, vol. 2014, pp. 15, 2014, ISBN: 1110-757X.
Abstract | Links | BibTeX | Tags: Ensemble methods, Laser milling, Neural networks, Support vector machines
@article{Santos2014,
title = {Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles},
author = {Pedro Santos and Daniel Teixidor and Jesús Maudes-Raedo and Joaquim Ciurana},
url = {https://www.hindawi.com/journals/jam/2014/439091/},
doi = {10.1155/2014/439091},
isbn = {1110-757X},
year = {2014},
date = {2014-04-17},
journal = {Journal of Applied Mathematics},
volume = {2014},
pages = {15},
abstract = {A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.},
keywords = {Ensemble methods, Laser milling, Neural networks, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.
Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
Tree ensemble construction using a GRASP-based heuristic and annealed randomness Journal Article
In: Information Fusion, vol. 20, no. 0, pp. 189–202, 2014, ISSN: 1566-2535.
Abstract | Links | BibTeX | Tags: Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, GRASP metahuristic, Random forest
@article{DiezPastor2014,
title = {Tree ensemble construction using a GRASP-based heuristic and annealed randomness},
author = {José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez},
url = {http://www.sciencedirect.com/science/article/pii/S1566253514000141},
doi = {10.1016/j.inffus.2014.01.009},
issn = {1566-2535},
year = {2014},
date = {2014-01-01},
journal = {Information Fusion},
volume = {20},
number = {0},
pages = {189–202},
abstract = {Abstract Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness. The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensemble is the randomness of the solution generation method of GRASP. A further key feature of the tree construction method for GAR-Forest is a decreasing level of randomness during the process of constructing the tree: maximum randomness at the root and minimum randomness at the leaves. The method is therefore named ``GAR'', GRASP with annealed randomness. The results conclusively demonstrate that G-Forest and GAR-Forest outperform Bagging, AdaBoost, MultiBoost, Random Forest and Random Subspaces. The results are even more convincing in the presence of noise, demonstrating the robustness of the method. The relationship between base classifier accuracy and their diversity is analysed by application of kappa-error diagrams and a variant of these called kappa-error relative movement diagrams.},
keywords = {Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, GRASP metahuristic, Random forest},
pubstate = {published},
tppubtype = {article}
}
Abstract Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness. The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensemble is the randomness of the solution generation method of GRASP. A further key feature of the tree construction method for GAR-Forest is a decreasing level of randomness during the process of constructing the tree: maximum randomness at the root and minimum randomness at the leaves. The method is therefore named ``GAR'', GRASP with annealed randomness. The results conclusively demonstrate that G-Forest and GAR-Forest outperform Bagging, AdaBoost, MultiBoost, Random Forest and Random Subspaces. The results are even more convincing in the presence of noise, demonstrating the robustness of the method. The relationship between base classifier accuracy and their diversity is analysed by application of kappa-error diagrams and a variant of these called kappa-error relative movement diagrams.
Díez-Pastor, José Francisco; Arnaiz-González, Alvar; García-Osorio, César; Rodríguez, Juan José
Segmentación de defectos en piezas de fundido usando umbrales adaptativos y ensembles Proceedings Article
In: XVII congreso español sobre tecnologías y lógica fuzzy, ESTYLF 2014, pp. 345-350, Zaragoza, Spain, 2014, ISBN: 978-84-15688-76-1.
BibTeX | Tags: Applied Machine Learning, Business intelligence, Data Mining
@inproceedings{ESTYLF2014a,
title = {Segmentación de defectos en piezas de fundido usando umbrales adaptativos y ensembles},
author = {José Francisco Díez-Pastor and Alvar Arnaiz-González and César García-Osorio and Juan José Rodríguez},
isbn = {978-84-15688-76-1},
year = {2014},
date = {2014-01-01},
booktitle = {XVII congreso español sobre tecnologías y lógica fuzzy, ESTYLF 2014},
pages = {345-350},
address = {Zaragoza, Spain},
keywords = {Applied Machine Learning, Business intelligence, Data Mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Arnaiz-González, Alvar; Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
Selección de instancias en regresión mediante discretización Proceedings Article
In: XVII congreso español sobre tecnologías y lógica fuzzy, ESTYLF 2014, pp. 351-356, Zaragoza, Spain, 2014, ISBN: 978-84-15688-76-1.
BibTeX | Tags: Data Mining, Instance selection
@inproceedings{ESTYLF2014b,
title = {Selección de instancias en regresión mediante discretización},
author = {Alvar Arnaiz-González and José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez},
isbn = {978-84-15688-76-1},
year = {2014},
date = {2014-01-01},
booktitle = {XVII congreso español sobre tecnologías y lógica fuzzy, ESTYLF 2014},
pages = {351-356},
address = {Zaragoza, Spain},
keywords = {Data Mining, Instance selection},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
López-Nozal, Carlos; Díez-Pastor, José Francisco; Maudes-Raedo, Jesús; Marticorena-Sánchez, Raúl
An Innovative Moodle Final Project Management Module for Bachelor and Master's Studies Journal Article
In: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 8, no. 3, pp. 103-110, 2013, ISSN: 1932-8540.
Abstract | Links | BibTeX | Tags: Bachelor and master's project management, e-learning, management models, Moodle
@article{López-Nozal2013,
title = {An Innovative Moodle Final Project Management Module for Bachelor and Master's Studies},
author = {Carlos López-Nozal and José Francisco Díez-Pastor and Jesús Maudes-Raedo and Raúl Marticorena-Sánchez},
url = {https://ieeexplore.ieee.org/document/6557438/},
doi = {10.1109/RITA.2013.2273109},
issn = {1932-8540},
year = {2013},
date = {2013-08-01},
journal = {IEEE Revista Iberoamericana de Tecnologias del Aprendizaje},
volume = {8},
number = {3},
pages = {103-110},
abstract = {Bachelor and master's qualifications include assignments that involve the preparation of final projects. Their underlying pedagogical model is often based on final or end-of-course projects, which carry a high number of ECTS credits (12 or more), to be completed over one semester. Each project, which simulates a real life professional situation, is completed by an individual student or a group of students in their last year of a university graduate or post-graduate course, in the case of engineering and architecture degrees. The special and complex perculiarities of these atypical assignments mean that they are not easily adapted to a virtual learning environment. This paper defines a management process for such projects and introduces a Moodle 1.9 module for its implementation.},
keywords = {Bachelor and master's project management, e-learning, management models, Moodle},
pubstate = {published},
tppubtype = {article}
}
Bachelor and master's qualifications include assignments that involve the preparation of final projects. Their underlying pedagogical model is often based on final or end-of-course projects, which carry a high number of ECTS credits (12 or more), to be completed over one semester. Each project, which simulates a real life professional situation, is completed by an individual student or a group of students in their last year of a university graduate or post-graduate course, in the case of engineering and architecture degrees. The special and complex perculiarities of these atypical assignments mean that they are not easily adapted to a virtual learning environment. This paper defines a management process for such projects and introduces a Moodle 1.9 module for its implementation.
Pardo, Carlos; Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
Rotation Forests for regression Journal Article
In: Applied Mathematics and Computation, vol. 219, no. 19, pp. 9914-9924, 2013, ISSN: 0096-3003.
Links | BibTeX | Tags: Data Mining, Regression, Rotation forest
@article{amcPardoDGR13,
title = {Rotation Forests for regression},
author = {Carlos Pardo and José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez},
doi = {10.1016/j.amc.2013.03.139},
issn = {0096-3003},
year = {2013},
date = {2013-01-01},
journal = {Applied Mathematics and Computation},
volume = {219},
number = {19},
pages = {9914-9924},
keywords = {Data Mining, Regression, Rotation forest},
pubstate = {published},
tppubtype = {article}
}
Díez-Pastor, José Francisco; García-Osorio, César; Barbero-García, Víctor; Blanco-Álamo, Alan
Imbalanced Learning Ensembles for Defect Detection in X-Ray Images Proceedings Article
In: Ali, Moonis; Bosse, Tibor; Hindriks, Koen V; Hoogendoorn, Mark; Jonker, Catholijn M; Treur, Jan (Ed.): 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, pp. 654-663, Amsterdam, The Netherland, 2013, ISBN: 978-3-642-38576-6.
Links | BibTeX | Tags: Applied Machine Learning, Business intelligence, Class-imbalanced problems, Data Mining
@inproceedings{ieaaieDiez-PastorGBB13,
title = {Imbalanced Learning Ensembles for Defect Detection in X-Ray Images},
author = {José Francisco Díez-Pastor and César García-Osorio and Víctor Barbero-García and Alan Blanco-Álamo},
editor = {Moonis Ali and Tibor Bosse and Koen V Hindriks and Mark Hoogendoorn and Catholijn M Jonker and Jan Treur},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84881385369&partnerID=40&md5=a5b5f8ad1a108c9da02b51a1346ddb10},
doi = {10.1007/978-3-642-38577-3_68},
isbn = {978-3-642-38576-6},
year = {2013},
date = {2013-01-01},
booktitle = {26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013},
pages = {654-663},
address = {Amsterdam, The Netherland},
keywords = {Applied Machine Learning, Business intelligence, Class-imbalanced problems, Data Mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodríguez, Juan José; Díez-Pastor, José Francisco; García-Osorio, César
Random Oracle Ensembles for Imbalanced Data Proceedings Article
In: Zhou, Zhi-Hua; Roli, Fabio; Kittler, Josef (Ed.): 11th International Workshop on Multiple Classifier Systems, MCS 2013, pp. 247-258, Nanjing, China, 2013, ISBN: 978-3-642-38066-2.
Links | BibTeX | Tags: Class-imbalanced problems, Data Mining, Ensemble methods, Random oracles
@inproceedings{mcsRodriguezDG13,
title = {Random Oracle Ensembles for Imbalanced Data},
author = {Juan José Rodríguez and José Francisco Díez-Pastor and César García-Osorio},
editor = {Zhi-Hua Zhou and Fabio Roli and Josef Kittler},
doi = {10.1007/978-3-642-38067-9_22},
isbn = {978-3-642-38066-2},
year = {2013},
date = {2013-01-01},
booktitle = {11th International Workshop on Multiple Classifier Systems, MCS 2013},
pages = {247-258},
address = {Nanjing, China},
crossref = {mcs2013},
keywords = {Class-imbalanced problems, Data Mining, Ensemble methods, Random oracles},
pubstate = {published},
tppubtype = {inproceedings}
}
García-Pedrajas, Nicolás; García-Osorio, César
Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections Journal Article
In: Progress in Artificial Intelligence, vol. 2, no. 1, pp. 29-44, 2013, ISSN: 2192-6352.
Links | BibTeX | Tags: Boosting, Class-imbalanced problems, Data Mining, Real-coded genetic algorithms
@article{PedrajasOsorio2013,
title = {Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections},
author = {Nicolás García-Pedrajas and César García-Osorio},
url = {http://dx.doi.org/10.1007/s13748-012-0028-4},
doi = {10.1007/s13748-012-0028-4},
issn = {2192-6352},
year = {2013},
date = {2013-01-01},
journal = {Progress in Artificial Intelligence},
volume = {2},
number = {1},
pages = {29-44},
publisher = {Springer-Verlag},
keywords = {Boosting, Class-imbalanced problems, Data Mining, Real-coded genetic algorithms},
pubstate = {published},
tppubtype = {article}
}
2012
García-Pedrajas, Nicolás; Maudes-Raedo, Jesús; García-Osorio, César; Rodríguez, Juan José
Supervised subspace projections for constructing ensembles of classifiers Journal Article
In: Information Sciences, vol. 193, pp. 1–21, 2012, ISSN: 0020-0255, (Accepted).
Links | BibTeX | Tags: Classification, Ensemble methods, Subspace methods, Supervised projections
@article{subespacios2012,
title = {Supervised subspace projections for constructing ensembles of classifiers},
author = {Nicolás García-Pedrajas and Jesús Maudes-Raedo and César García-Osorio and Juan José Rodríguez},
url = {http://www.sciencedirect.com/science/article/pii/S0020025511003306},
doi = {10.1016/j.ins.2011.06.023},
issn = {0020-0255},
year = {2012},
date = {2012-06-01},
journal = {Information Sciences},
volume = {193},
pages = {1–21},
publisher = {Elsevier},
note = {Accepted},
keywords = {Classification, Ensemble methods, Subspace methods, Supervised projections},
pubstate = {published},
tppubtype = {article}
}
Maudes, Jesús; Rodríguez, Juan José; García-Osorio, César; García-Pedrajas, Nicolás
Random Feature Weights for Decision Tree Ensemble Construction Journal Article
In: Information Fusion, vol. 13, no. 1, pp. 20-30, 2012, ISSN: 1566-2535.
Links | BibTeX | Tags: Bagging, Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, Random forest
@article{RFW2012,
title = {Random Feature Weights for Decision Tree Ensemble Construction},
author = {Jesús Maudes and Juan José Rodríguez and César García-Osorio and Nicolás García-Pedrajas},
doi = {10.1016/j.inffus.2010.11.004},
issn = {1566-2535},
year = {2012},
date = {2012-01-01},
journal = {Information Fusion},
volume = {13},
number = {1},
pages = {20-30},
keywords = {Bagging, Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, Random forest},
pubstate = {published},
tppubtype = {article}
}
Pardo, Carlos; Díez-Pastor, José Francisco; García-Pedrajas, Nicolás; Rodríguez, Juan José; García-Osorio, César
Linear projections — An Experimental Study for Regression Problems Proceedings Article
In: Carmona, Pedro Latorre; Sánchez, Salvador J; Fred, Ana (Ed.): 1st International Conference on Patter Recognition Applications and Methods (ICPRAM), pp. 198–204, SciTePress — Science and Technology Publications, Villamoura, Portugal, 2012, ISBN: 978-989-8425-98-0.
BibTeX | Tags: Data Mining, Linear projections, Regression
@inproceedings{ICPRAM2012,
title = {Linear projections — An Experimental Study for Regression Problems},
author = {Carlos Pardo and José Francisco Díez-Pastor and Nicolás García-Pedrajas and Juan José Rodríguez and César García-Osorio},
editor = {Pedro Latorre Carmona and Salvador J Sánchez and Ana Fred},
isbn = {978-989-8425-98-0},
year = {2012},
date = {2012-01-01},
booktitle = {1st International Conference on Patter Recognition Applications and Methods (ICPRAM)},
pages = {198–204},
publisher = {SciTePress — Science and Technology Publications},
address = {Villamoura, Portugal},
keywords = {Data Mining, Linear projections, Regression},
pubstate = {published},
tppubtype = {inproceedings}
}
Díez-Pastor, José Francisco; Bustillo, Andrés; Quintana, Guillem; García-Osorio, César
Boosting Projections to improve surface roughness prediction in high-torque milling operations Journal Article
In: Soft Computing, vol. 16, no. 8, pp. 1427-1437, 2012, ISSN: 1432-7643 (Print) 1433-7479 (Online).
Links | BibTeX | Tags: Applied Machine Learning, Business intelligence, Data Mining, Ensemble methods
@article{BPforIndustrialData2012,
title = {Boosting Projections to improve surface roughness prediction in high-torque milling operations},
author = {José Francisco Díez-Pastor and Andrés Bustillo and Guillem Quintana and César García-Osorio},
url = {http://dx.doi.org/10.1007/s00500-012-0846-0},
doi = {10.1007/s00500-012-0846-0},
issn = {1432-7643 (Print) 1433-7479 (Online)},
year = {2012},
date = {2012-01-01},
journal = {Soft Computing},
volume = {16},
number = {8},
pages = {1427-1437},
keywords = {Applied Machine Learning, Business intelligence, Data Mining, Ensemble methods},
pubstate = {published},
tppubtype = {article}
}
Rodríguez, Juan José; Díez-Pastor, José Francisco; Maudes, Jesús; García-Osorio, César
Disturbing Neighbors Ensembles of Trees for Imbalanced Data Proceedings Article
In: Wani, Arif M; Khoshgoftaar, Taghi; Zhu, Xingquan (Hill); Seliya, Naeem (Ed.): 11th International Conference on Machine Learning and Applications, ICMLA 2012, pp. 83-88, IEEE, Boca Ratón, EEUU, 2012, ISBN: 978-0-7695-4913-2.
Links | BibTeX | Tags: Class-imbalanced problems, Data Mining, Decision trees, Disturbing neighbors, Ensemble methods
@inproceedings{RDMG12,
title = {Disturbing Neighbors Ensembles of Trees for Imbalanced Data},
author = {Juan José Rodríguez and José Francisco Díez-Pastor and Jesús Maudes and César García-Osorio},
editor = {Arif M Wani and Taghi Khoshgoftaar and Xingquan (Hill) Zhu and Naeem Seliya},
doi = {10.1109/ICMLA.2012.181},
isbn = {978-0-7695-4913-2},
year = {2012},
date = {2012-01-01},
booktitle = {11th International Conference on Machine Learning and Applications, ICMLA 2012},
volume = {2},
pages = {83-88},
publisher = {IEEE},
address = {Boca Ratón, EEUU},
keywords = {Class-imbalanced problems, Data Mining, Decision trees, Disturbing neighbors, Ensemble methods},
pubstate = {published},
tppubtype = {inproceedings}
}
Bermejo-Teson, Daniel; Díez-Pastor, José Francisco; Arnaiz-González, Álvar; García-Osorio, César
Spectralizer: a tool for visualization of spectral clustering algorithms Proceedings Article
In: Proceedings of the 4th international conference on education and new learning technologies (EDULEARN12), pp. 1985–1992, IATED, Barcelona, Spain, 2012, ISSN: 2340-1117, (2nd-4th July 2012).
Links | BibTeX | Tags: Clustering, Computer Science teaching, Spectral clustering
@inproceedings{Spectralizer:EDULEARN2012,
title = {Spectralizer: a tool for visualization of spectral clustering algorithms},
author = {Daniel Bermejo-Teson and José Francisco Díez-Pastor and Álvar Arnaiz-González and César García-Osorio},
url = {http://library.iated.org/view/BERMEJOTESON2012SPE},
issn = {2340-1117},
year = {2012},
date = {2012-00-01},
booktitle = {Proceedings of the 4th international conference on education and new learning technologies (EDULEARN12)},
pages = {1985–1992},
publisher = {IATED},
address = {Barcelona, Spain},
note = {2nd-4th July 2012},
keywords = {Clustering, Computer Science teaching, Spectral clustering},
pubstate = {published},
tppubtype = {inproceedings}
}
Arnaiz-González, Álvar; Diez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
Tool for supporting the teaching of instance selection algorithms Proceedings Article
In: Proceedings of the 4th international conference on education and new learning technologies (EDULEARN12), pp. 6088–6096, IATED, Barcelona, Spain, 2012, ISSN: 2340-1117, (2nd-4th July 2012).
Links | BibTeX | Tags: Computer Science teaching, Instance selection
@inproceedings{ISBur:EDULEARN2012,
title = {Tool for supporting the teaching of instance selection algorithms},
author = {Álvar Arnaiz-González and José Francisco Diez-Pastor and César García-Osorio and Juan José Rodríguez},
url = {http://library.iated.org/view/ARNAIZGONZLEZ2012TOO},
issn = {2340-1117},
year = {2012},
date = {2012-00-01},
booktitle = {Proceedings of the 4th international conference on education and new learning technologies (EDULEARN12)},
pages = {6088–6096},
publisher = {IATED},
address = {Barcelona, Spain},
note = {2nd-4th July 2012},
keywords = {Computer Science teaching, Instance selection},
pubstate = {published},
tppubtype = {inproceedings}
}
Arnaiz-González, Álvar; Diez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
Herramienta de apoyo a la docencia de algoritmos de selección de instancias Proceedings Article
In: Actas de las XVIII Jornadas de Enseñanza Universitaria de Informática (JENUI 2012), pp. 33–40, Ciudad Real,Spain, 2012, ISBN: 978–84–615–7157–4, (10-13 de julio 2012).
BibTeX | Tags: Computer Science teaching, Instance selection
@inproceedings{JENUI2012,
title = {Herramienta de apoyo a la docencia de algoritmos de selección de instancias},
author = {Álvar Arnaiz-González and José Francisco Diez-Pastor and César García-Osorio and Juan José Rodríguez},
isbn = {978–84–615–7157–4},
year = {2012},
date = {2012-00-01},
booktitle = {Actas de las XVIII Jornadas de Enseñanza Universitaria de Informática (JENUI 2012)},
pages = {33–40},
address = {Ciudad Real,Spain},
note = {10-13 de julio 2012},
keywords = {Computer Science teaching, Instance selection},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
García-Pedrajas, Nicolás; García-Osorio, César
Constructing ensembles of classifiers using supervised projection methods based on misclassified instances Journal Article
In: Expert Systems with Applications, vol. 38, no. 1, pp. 343–359, 2011, ISSN: 0957-4174.
Links | BibTeX | Tags: Boosting, Classification, Data Mining, Linear projections, Subspace methods
@article{ensemblesProjections2011,
title = {Constructing ensembles of classifiers using supervised projection methods based on misclassified instances},
author = {Nicolás García-Pedrajas and César García-Osorio},
url = {http://www.sciencedirect.com/science/article/B6V03-50GJ2J0-7/2/6b1890282b8bfb900f1174dc7a027a9c},
doi = {10.1016/j.eswa.2010.06.072},
issn = {0957-4174},
year = {2011},
date = {2011-01-01},
journal = {Expert Systems with Applications},
volume = {38},
number = {1},
pages = {343–359},
keywords = {Boosting, Classification, Data Mining, Linear projections, Subspace methods},
pubstate = {published},
tppubtype = {article}
}
Maudes, Jesús; Rodríguez, Juan José; García-Osorio, César; Pardo, Carlos
Random projections for linear SVM ensembles Journal Article
In: Applied Intelligence, vol. 34, pp. 347-359, 2011, ISSN: 0924-669X, 1573-7497, (10.1007/s10489-011-0283-2).
Links | BibTeX | Tags: Data Mining, Ensemble methods, Support vector machines
@article{RandomProjectionsLinearSVMs,
title = {Random projections for linear SVM ensembles},
author = {Jesús Maudes and Juan José Rodríguez and César García-Osorio and Carlos Pardo},
url = {http://dx.doi.org/10.1007/s10489-011-0283-2},
doi = {10.1007/s10489-011-0283-2},
issn = {0924-669X, 1573-7497},
year = {2011},
date = {2011-01-01},
journal = {Applied Intelligence},
volume = {34},
pages = {347-359},
publisher = {Springer Netherlands},
note = {10.1007/s10489-011-0283-2},
keywords = {Data Mining, Ensemble methods, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
Bustillo, Andres; Díez-Pastor, José Francisco; Quintana, Guillem; García-Osorio, César
Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 57, no. 5–8, pp. 1-12, 2011, ISSN: 0268-3768, (10.1007/s00170-011-3300-z).
Links | BibTeX | Tags: Applied Machine Learning, Business intelligence, Neural networks
@article{Bustillo2011,
title = {Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations},
author = {Andres Bustillo and José Francisco Díez-Pastor and Guillem Quintana and César García-Osorio},
url = {http://dx.doi.org/10.1007/s00170-011-3300-z},
doi = {10.1007/s00170-011-3300-z},
issn = {0268-3768},
year = {2011},
date = {2011-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {57},
number = {5–8},
pages = {1-12},
publisher = {Springer London},
note = {10.1007/s00170-011-3300-z},
keywords = {Applied Machine Learning, Business intelligence, Neural networks},
pubstate = {published},
tppubtype = {article}
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Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José; Bustillo, Andrés
GRASP Forest: A New Ensemble Method for Trees Proceedings Article
In: Sansone, Carlo; Kittler, Josef; Roli, Fabio (Ed.): 10th International Workshop on Multiple Classifier Systems, MCS 2011, pp. 66-75, Springer-Verlag, Naples, Italy, 2011, ISSN: 0302-9743.
Links | BibTeX | Tags: Data Mining, Decision trees, Ensemble methods
@inproceedings{Diez-Pastor2011,
title = {GRASP Forest: A New Ensemble Method for Trees},
author = {José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez and Andrés Bustillo},
editor = {Carlo Sansone and Josef Kittler and Fabio Roli},
doi = {10.1007/978-3-642-21557-5_9},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
booktitle = {10th International Workshop on Multiple Classifier Systems, MCS 2011},
volume = {6713},
pages = {66-75},
publisher = {Springer-Verlag},
address = {Naples, Italy},
series = {Lecture Notes in Computer Sciences},
keywords = {Data Mining, Decision trees, Ensemble methods},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodríguez, Juan José; Díez-Pastor, José Francisco; García-Osorio, César
Ensembles of Decision Trees for Imbalanced Data Proceedings Article
In: Sansone, Carlo; Kittler, Josef; Roli, Fabio (Ed.): 10th International Workshop on Multiple Classifier Systems, MCS 2011, pp. 76-85, Springer-Verlag, Naples, Italy, 2011, ISSN: 0302-9743.
Links | BibTeX | Tags: Class-imbalanced problems, Data Mining, Decision trees, Ensemble methods
@inproceedings{Rodriguez2011,
title = {Ensembles of Decision Trees for Imbalanced Data},
author = {Juan José Rodríguez and José Francisco Díez-Pastor and César García-Osorio},
editor = {Carlo Sansone and Josef Kittler and Fabio Roli},
doi = {10.1007/978-3-642-21557-5_10},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
booktitle = {10th International Workshop on Multiple Classifier Systems, MCS 2011},
volume = {6713},
pages = {76-85},
publisher = {Springer-Verlag},
address = {Naples, Italy},
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keywords = {Class-imbalanced problems, Data Mining, Decision trees, Ensemble methods},
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tppubtype = {inproceedings}
}
Pardo, Carlos; Rodríguez, Juan José; Díez-Pastor, José Francisco; García-Osorio, César
Random Oracles for Regression Ensembles Book Chapter
In: Okun, Oleg; Valentini, Giorgio; Re, Matteo (Ed.): Ensembles in Machine Learning Applications, vol. 373, pp. 181-199, Springer, 2011, ISBN: 978-3-642-22909-1.
Links | BibTeX | Tags: Data Mining, Random oracles, Regression ensembles
@inbook{PRDG11,
title = {Random Oracles for Regression Ensembles},
author = {Carlos Pardo and Juan José Rodríguez and José Francisco Díez-Pastor and César García-Osorio},
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doi = {10.1007/978-3-642-22910-7_11},
isbn = {978-3-642-22909-1},
year = {2011},
date = {2011-01-01},
booktitle = {Ensembles in Machine Learning Applications},
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pages = {181-199},
publisher = {Springer},
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tppubtype = {inbook}
}
Rodríguez, Juan José; Díez-Pastor, José Francisco; García-Osorio, César; Santos, Pedro
Using Model Trees and their Ensembles for Imbalanced Data Proceedings Article
In: Lozano, Jose A; Gámez, José A; Moreno, José A (Ed.): Advances in Artificial Intelligence: 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, pp. 94–103, Springer, La Laguna, Spain, 2011, ISBN: 978-3-642-25273-0.
BibTeX | Tags: Class-imbalanced problems, Data Mining, Decision trees, Ensemble methods
@inproceedings{RDGS11,
title = {Using Model Trees and their Ensembles for Imbalanced Data},
author = {Juan José Rodríguez and José Francisco Díez-Pastor and César García-Osorio and Pedro Santos},
editor = {Jose A Lozano and José A Gámez and José A Moreno},
isbn = {978-3-642-25273-0},
year = {2011},
date = {2011-01-01},
booktitle = {Advances in Artificial Intelligence: 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011},
volume = {7023},
pages = {94–103},
publisher = {Springer},
address = {La Laguna, Spain},
series = {Lecture Notes in Computer Science},
keywords = {Class-imbalanced problems, Data Mining, Decision trees, Ensemble methods},
pubstate = {published},
tppubtype = {inproceedings}
}
Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José
GRASP Forest for regression: GRASP Metaheuristic Applied to the Construction of Ensembles of Regression Trees Proceedings Article
In: CAEPIA 2011, 2011.
BibTeX | Tags: Data Mining, Decision trees, Regression ensembles
@inproceedings{DGR11,
title = {GRASP Forest for regression: GRASP Metaheuristic Applied to the Construction of Ensembles of Regression Trees},
author = {José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez},
year = {2011},
date = {2011-01-01},
booktitle = {CAEPIA 2011},
keywords = {Data Mining, Decision trees, Regression ensembles},
pubstate = {published},
tppubtype = {inproceedings}
}
Pardo, Carlos; Rodríguez, Juan José; Díez-Pastor, José Francisco; García-Osorio, César
Random Oracles for Regression Ensembles Book Chapter
In: Ensembles in Machine Learning Applications, vol. 373, pp. 181-199, 2011, ISSN: 1860-949X.
Links | BibTeX | Tags: Data Mining, Random oracles, Regression ensembles
@inbook{ROforReg,
title = {Random Oracles for Regression Ensembles},
author = {Carlos Pardo and Juan José Rodríguez and José Francisco Díez-Pastor and César García-Osorio},
doi = {10.1007/978-3-642-22910-7},
issn = {1860-949X},
year = {2011},
date = {2011-01-01},
booktitle = {Ensembles in Machine Learning Applications},
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2010
Fyfe, C; Tino, P; Charles, D; García-Osorio, C; Yin, H (Ed.)
11th International Conference on Intelligent Data Engineering and Automated Learning Proceedings
IDEAL2010 Springer, 2010, ISBN: 978-3-642-15380-8.
BibTeX | Tags: Applied Machine Learning, Business intelligence, Data Mining
@proceedings{fyfe:ideal2010,
title = {11th International Conference on Intelligent Data Engineering and Automated Learning},
editor = {C Fyfe and P Tino and D Charles and C García-Osorio and H Yin},
isbn = {978-3-642-15380-8},
year = {2010},
date = {2010-09-01},
publisher = {Springer},
organization = {IDEAL2010},
keywords = {Applied Machine Learning, Business intelligence, Data Mining},
pubstate = {published},
tppubtype = {proceedings}
}
García-Osorio, César; Haro-García, Aida; García-Pedrajas, Nicolás
Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts Journal Article
In: Artif. Intell., vol. 174, no. 5-6, pp. 410–441, 2010, ISSN: 0004-3702.
Links | BibTeX | Tags: Big data, Data Mining, Instance selection
@article{1746771,
title = {Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts},
author = {César García-Osorio and Aida Haro-García and Nicolás García-Pedrajas},
doi = {10.1016/j.artint.2010.01.001},
issn = {0004-3702},
year = {2010},
date = {2010-01-01},
journal = {Artif. Intell.},
volume = {174},
number = {5-6},
pages = {410–441},
publisher = {Elsevier Science Publishers Ltd.},
address = {Essex, UK},
keywords = {Big data, Data Mining, Instance selection},
pubstate = {published},
tppubtype = {article}
}