期刊
APPLIED SOFT COMPUTING
卷 17, 期 -, 页码 23-31出版社
ELSEVIER
DOI: 10.1016/j.asoc.2013.12.013
关键词
Support vector machines; Imbalanced accuracy; Cost-sensitive schemes; Classification; Imbalanced databases
资金
- Spanish Ministry of Science and Innovation [TIN2009-14378-C02-01, TIN2012-38416-C03-01]
- Simon of Andalusian Regional Ministry of Economy [TIC-8052]
- Central University of Venezuela [PG-03-7678-2009/1]
A new support vector machine, SVM, is introduced, called GSVM, which is specially designed for bi-classification problems where balanced accuracy between classes is the objective. Starting from a standard SVM, the GSVM is obtained from a low-cost post-processing strategy by modifying the initial bias. Thus, the bias for GSVM is calculated by moving the original bias in the SVM to improve the geometric mean between the true positive rate and the true negative rate. The proposed solution neither modifies the original optimization problem for SVM training, nor introduces new hyper-parameters. Experimentation carried out on a high number of databases (23) shows GSVM obtaining the desired balanced accuracy between classes. Furthermore, its performance improves well-known cost-sensitive schemes for SVM, without adding complexity or computational cost. (C) 2013 Elsevier B. V. All rights reserved.
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