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Mathematical Problems in Engineering
Volume 2014, Article ID 358942, 14 pages
Research Article

A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China

Received 22 November 2013; Revised 23 January 2014; Accepted 14 February 2014; Published 30 March 2014

Academic Editor: Panos Liatsis

Copyright © 2014 Qing-Yan Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling, RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show that RotEasy possesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.