Publications

@article{Kuncheva2019,

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://link.springer.com/article/10.1007/s13748-019-00172-4?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst&utm_source=ArticleAuthorContributingOnlineFirst&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorContributingOnlineFirst_20190209},

doi = {10.1007/s13748-019-00172-4},

issn = {2192-6352},

year  = {2019},

date = {2019-06-01},

journal = {Progress in Artificial Intelligence},

volume = {8},

number = {2},

pages = {215-228},

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, Theoretical perspective},

pubstate = {published},

tppubtype = {article}

}

@article{Faithfull2019,

title = {Combining univariate approaches for ensemble change detection in multivariate data},

author = {William J Faithfull and Juan José Rodríguez and Ludmila I Kuncheva},

url = {https://www.sciencedirect.com/science/article/pii/S1566253517301239},

doi = {10.1016/j.inffus.2018.02.003},

issn = {1566-2535},

year  = {2019},

date = {2019-01-01},

journal = {Information Fusion},

volume = {45},

pages = {202-214},

abstract = {Detecting change in multivariate data is a challenging problem, especially when class labels are not available. There is a large body of research on univariate change detection, notably in control charts developed originally for engineering applications. We evaluate univariate change detection approaches —including those in the MOA framework — built into ensembles where each member observes a feature in the input space of an unsupervised change detection problem. We present a comparison between the ensemble combinations and three established ‘pure’ multivariate approaches over 96 data sets, and a case study on the KDD Cup 1999 network intrusion detection dataset. We found that ensemble combination of univariate methods consistently outperformed multivariate methods on the four experimental metrics.},

keywords = {Change detection, Ensemble methods, Multivariate data},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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, SMOTE, Undersampling},

pubstate = {published},

tppubtype = {article}

}

@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, SMOTE, Undersampling},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{cgosorio07boosting,

title = {Nonlinear ``boosting'' projections for ensemble construction},

author = {Nicolás García-Pedrajas and César García-Osorio and Colin Fyfe},

url = {http://jmlr.csail.mit.edu/papers/volume8/garcia-pedrajas07a/garcia-pedrajas07a.pdf},

issn = {1532-4435},

year  = {2007},

date = {2007-01-01},

journal = {Journal of Machine Learning Research},

volume = {8},

pages = {1--33},

abstract = {In this paper we propose a novel approach for ensemble construction based on the use of nonlinear 

projections to achieve both accuracy and diversity of individual classifiers. The proposed approach 

combines the philosophy of boosting, putting more effort on difficult instances, with the basis of 

the random subspace method. Our main contribution is that instead of using a random subspace, 

we construct a projection taking into account the instances which have posed most difficulties to 

previous classifiers. In this way, consecutive nonlinear projections are created by a neural network 

trained using only incorrectly classified instances. The feature subspace induced by the hidden layer 

of this network is used as the input space to a new classifier. The method is compared with bagging 

and boosting techniques, showing an improved performance on a large set of 44 problems from the 

UCI Machine Learning Repository. An additional study showed that the proposed approach is less 

sensitive to noise in the data than boosting methods.},

keywords = {Boosting, Classifier ensembles, Data Mining, Ensemble methods, Neural networks, Nonlinear projections},

pubstate = {published},

tppubtype = {article}

}