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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 5423204, 10 pages
Research Article

Imbalanced Learning Based on Logistic Discrimination

1School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
2School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China

Received 10 July 2015; Revised 23 October 2015; Accepted 26 October 2015

Academic Editor: José David Martín-Guerrero

Copyright © 2016 Huaping Guo 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.


In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its performance. To fully consider the class imbalance, we design a new cost function which takes into account the accuracies of both positive class and negative class as well as the precision of positive class. Unlike traditional logistic discrimination, the proposed method learns its parameters by maximizing the proposed cost function. Experimental results show that, compared with other state-of-the-art methods, the proposed one shows significantly better performance on measures of recall, -mean, -measure, AUC, and accuracy.