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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