An improved fuzzy support vector machine for credit rating

Authors:
Yanyou Hao;Zhongxian Chi;Deqin Yan;Xun Yue
Affiliations:
Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China and Dalian Branch of CCB, Dalian, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China;Department of Computer Science, Liaoning Normal University, Dalian, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China
Venue:
NPC'07 Proceedings of the 2007 IFIP international conference on Network and parallel computing
Year:
2007

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Abstract

In order to classify data with noises or outliers, Fuzzy support vector machine (FSVM) improve the generalization power of traditional SVM by assigning a fuzzy membership to each input data point. In this paper, an improved FSVM based on vague sets is proposed by assigning a truth-membership and a false-membership to each data point. And we reformulate the improved FSVM so that different input points can make different contributions to decision hyperplane. The effectiveness of the improved FSVM is verified in credit rating; the experiment results show that our method is promising.