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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 196256, 6 pages
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.
- 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.
- 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.
- 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.
- Giorgio Valentini, “Hierarchical Ensemble Methods for Protein Function Prediction,” ISRN Bioinformatics, vol. 2014, pp. 1–34, 2014.