2017
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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, 30 (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. |
2014
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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, 20 (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. |
2012
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Maudes, Jesús; Rodríguez, Juan José; García-Osorio, César; García-Pedrajas, Nicolás Random Feature Weights for Decision Tree Ensemble Construction Journal Article In: Information Fusion, 13 (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}
}
|
Rodríguez, Juan José; Díez-Pastor, José Francisco; Maudes, Jesús; García-Osorio, César Disturbing Neighbors Ensembles of Trees for Imbalanced Data Inproceedings 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}
}
|
2011
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Díez-Pastor, José Francisco; García-Osorio, César; Rodríguez, Juan José; Bustillo, Andrés GRASP Forest: A New Ensemble Method for Trees Inproceedings 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 Inproceedings 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},
series = {LNCS},
keywords = {Class-imbalanced problems, Data Mining, Decision trees, Ensemble methods},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rodríguez, Juan José; Díez-Pastor, José Francisco; García-Osorio, César; Santos, Pedro Using Model Trees and their Ensembles for Imbalanced Data Inproceedings 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 Inproceedings 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}
}
|
2010
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Rodríguez, Juan José; García-Osorio, César; Maudes, Jesús; Díez-Pastor, José Francisco An Experimental Study on Ensembles of Functional Trees Inproceedings In: Gayar, Neamat El; Kittler, Josef; Roli, Fabio (Ed.): 9th International Workshop on Multiple Classifier Systems, MCS 2010, pp. 64-73, Cairo, Egypt, 2010, ISBN: 978-3-642-12126-5. Links | BibTeX | Tags: Data Mining, Decision trees, Ensemble methods @inproceedings{RGMD10,
title = {An Experimental Study on Ensembles of Functional Trees},
author = {Juan José Rodríguez and César García-Osorio and Jesús Maudes and José Francisco Díez-Pastor},
editor = {Neamat El Gayar and Josef Kittler and Fabio Roli},
doi = {10.1007/978-3-642-12127-2_7},
isbn = {978-3-642-12126-5},
year = {2010},
date = {2010-01-01},
booktitle = {9th International Workshop on Multiple Classifier Systems, MCS 2010},
volume = {5997},
pages = {64-73},
address = {Cairo, Egypt},
series = {Lecture Notes in Computer Science},
keywords = {Data Mining, Decision trees, Ensemble methods},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rodríguez, Juan José; García-Osorio, César; Maudes, Jesús Forests of Nested Dichotomies Journal Article In: Pattern Recognition Letters, 31 (2), pp. 125-132, 2010, ISSN: 0167-8655. Links | BibTeX | Tags: Data Mining, Decision trees, Ensemble methods @article{RGM10,
title = {Forests of Nested Dichotomies},
author = {Juan José Rodríguez and César García-Osorio and Jesús Maudes},
doi = {10.1016/j.patrec.2009.09.015},
issn = {0167-8655},
year = {2010},
date = {2010-01-01},
journal = {Pattern Recognition Letters},
volume = {31},
number = {2},
pages = {125-132},
keywords = {Data Mining, Decision trees, Ensemble methods},
pubstate = {published},
tppubtype = {article}
}
|
2009
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Maudes, Jesús; Rodríguez, Juan José; García-Osorio, César Disturbing Neighbors Ensembles for Linear SVM Inproceedings In: Benediktsson, Jon Atli; Kittler, Josef; Roli, Fabio (Ed.): 8th International Workshop on Multiple Classifier Systems, MCS 2009, pp. 191–200, Springer-Verlag, Reykjavik, Iceland, 2009, ISBN: 978-3-642-02325-5. Links | BibTeX | Tags: Classifier ensembles, Data Mining, Decision trees, Disturbing neighbors, Ensemble methods, Support vector machines @inproceedings{MRG09a,
title = {Disturbing Neighbors Ensembles for Linear SVM},
author = {Jesús Maudes and Juan José Rodríguez and César García-Osorio},
editor = {Jon Atli Benediktsson and Josef Kittler and Fabio Roli},
doi = {10.1007/978-3-642-02326-2_20},
isbn = {978-3-642-02325-5},
year = {2009},
date = {2009-01-01},
booktitle = {8th International Workshop on Multiple Classifier Systems, MCS 2009},
volume = {5519},
pages = {191--200},
publisher = {Springer-Verlag},
address = {Reykjavik, Iceland},
series = {Lecture Notes in Computer Science},
keywords = {Classifier ensembles, Data Mining, Decision trees, Disturbing neighbors, Ensemble methods, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2008
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Maudes-Raedo, Jesús; Rodríguez, Juan José; García-Osorio, César Disturbing Neighbors Diversity for Decision Forest Inproceedings In: Valentini, Giorgio; Okun, Oleg (Ed.): Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA 2008), pp. 67–71, Patras, Grecia, 2008, ISBN: 978-84-612-4475-1. BibTeX | Tags: Classifier ensembles, Data Mining, Decision trees, Disturbing neighbors, Ensemble methods @inproceedings{SUEMA2008:DisturbingNeighbors,
title = {Disturbing Neighbors Diversity for Decision Forest},
author = {Jesús Maudes-Raedo and Juan José Rodríguez and César García-Osorio},
editor = {Giorgio Valentini and Oleg Okun},
isbn = {978-84-612-4475-1},
year = {2008},
date = {2008-00-01},
booktitle = {Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA 2008)},
pages = {67--71},
address = {Patras, Grecia},
keywords = {Classifier ensembles, Data Mining, Decision trees, Disturbing neighbors, Ensemble methods},
pubstate = {published},
tppubtype = {inproceedings}
}
|
