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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.

Citations to this Article [7 citations]

The following is the list of published articles that have cited the current article.

  • Li Yijing, Guo Haixiang, Liu Xiao, Li Yanan, and Li Jinling, “Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data,” Knowledge-Based Systems, vol. 94, pp. 88–104, 2016. View at Publisher · View at Google Scholar
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  • Wei Wang, Yongxiao Yang, Jianxin Yin, and Xinqi Gong, “Different protein-protein interface patterns predicted by different machine learning methods,” Scientific Reports, vol. 7, no. 1, 2017. View at Publisher · View at Google Scholar
  • Lincy Meera Mathews, and Hari Seetha, “On improving the classification of imbalanced data,” Cybernetics and Information Technologies, vol. 17, no. 1, pp. 45–62, 2017. View at Publisher · View at Google Scholar
  • Bahareh Nikpour, and Hossein Nezamabadi-pour, “HTSS: a hyper-heuristic training set selection method for imbalanced data sets,” Iran Journal of Computer Science, 2018. View at Publisher · View at Google Scholar