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Diversity techniques improve the performance of the best imbalance learning ensembles

Diez-Pastor, J.F. and Rodriguez, J.J. and Garcia-Osorio, C.I. and Kuncheva, L.I. (2015) Diversity techniques improve the performance of the best imbalance learning ensembles. Information Sciences, 325. pp. 98-117. DOI: 10.1016/j.ins.2015.07.025

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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

Item Type: Article
Subjects: Research Publications
Departments: College of Physical and Applied Sciences > School of Computer Science
Date Deposited: 05 Nov 2015 04:46
Last Modified: 05 Nov 2015 04:46
ISSN: 0020-0255
URI: http://e.bangor.ac.uk/id/eprint/5816
Identification Number: DOI: 10.1016/j.ins.2015.07.025
Publisher: Elsevier
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