FUZZY INTEGRATING MULTIPLE SVM CLASSIFIERS AND ITS APPLICATION IN CREDIT SCORING

摘要：

This paper presents a method of combining support vector machine (SVM) based on fuzzy integral. The classification has two steps: first map individual SVM classifiers decision values, which are good representatives of confidence, to memberships, second aggregate these memberships by fuzzy integral to obtain the final decision.Experimental results on two public datasets indicate that the performance of the proposed method outperforms the three conventional combining methods: single best, majority-rule ensemble and weighted-majority-rule ensemble. It clearly shows that the method has a great potential to find successful application in credit scoring area.

关键词： Support vector machine Ensemble Credit scoring Fuzzy integral

作者: YONG-QIAO WANG JUN WU

作者单位: College of Finance, Zhejiang Gongshang University, Hangzhou 310018, China School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 1008

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3621-3626

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）