Diversity techniques improve the performance of the best imbalance learning ensembles

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...

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Bibliographic Details
Published in:Information sciences Vol. 325; pp. 98 - 117
Main Authors: Díez-Pastor, José F., Rodríguez, Juan J., García-Osorio, César I., Kuncheva, Ludmila I.
Format: Journal Article
Language:English
Published: Elsevier Inc 20.12.2015
Subjects:
Classifier ensembles
Classifiers
Diversity
Forests
Fraud
Imbalanced data sets
Learning
Oversampling
Performance enhancement
Repositories
Rotation forest
SMOTE
Undersampling
Undersampling
Rotation forest
SMOTE
Imbalanced data sets
Diversity
Classifier ensembles
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary: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.
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ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2015.07.025