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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 196256, 6 pages
http://dx.doi.org/10.1155/2013/196256
Research Article

A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

School of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi, Dalian, Liaoning 116081, China

Received 28 December 2012; Accepted 25 March 2013

Academic Editor: Jianhong (Cecilia) Xia

Copyright © 2013 Yong Zhang and Dapeng Wang. 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 [5 citations]

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

  • Qing-Yan Yin, Jiang-She Zhang, Chun-Xia Zhang, and Sheng-Cai Liu, “An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance,” Mathematical Problems in Engineering, vol. 2013, pp. 1–12, 2013. View at Publisher · View at Google Scholar
  • Yong Zhang, Panpan Fu, Wenzhe Liu, and Guolong Chen, “Imbalanced data classification based on scaling kernel-based support vector machine,” Neural Computing & Applications, vol. 25, no. 3-4, pp. 927–935, 2014. View at Publisher · View at Google Scholar
  • Myoung-Jong Kim, Dae-Ki Kang, and Hong Bae Kim, “Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction,” Expert Systems with Applications, 2014. View at Publisher · View at Google Scholar
  • Shehzad Khalid, Sannia Arshad, Sohail Jabbar, and Seungmin Rho, “Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level,” The Scientific World Journal, vol. 2014, pp. 1–14, 2014. View at Publisher · View at Google Scholar
  • Giorgio Valentini, “Hierarchical Ensemble Methods for Protein Function Prediction,” ISRN Bioinformatics, vol. 2014, pp. 1–34, 2014. View at Publisher · View at Google Scholar