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Imbalanced Learning Based on Logistic Discrimination
Joint Authors
Liu, Hongbing
Guo, Huaping
Zhi, Weimei
Xu, Mingliang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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, g-mean, f-measure, AUC, and accuracy.
American Psychological Association (APA)
Guo, Huaping& Zhi, Weimei& Liu, Hongbing& Xu, Mingliang. 2016. Imbalanced Learning Based on Logistic Discrimination. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099702
Modern Language Association (MLA)
Guo, Huaping…[et al.]. Imbalanced Learning Based on Logistic Discrimination. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1099702
American Medical Association (AMA)
Guo, Huaping& Zhi, Weimei& Liu, Hongbing& Xu, Mingliang. Imbalanced Learning Based on Logistic Discrimination. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099702
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099702