Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection

doi:10.1016/j.eswa.2011.06.023

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	Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection
	Yu, Lean 1,2; Yao, Xiao 1; Wang, Shouyang1 ; Lai, K. K.3
	2011-11-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	38 期号:12 页码:15392-15399
摘要	Support vector machines (SVM) is proved to be one of the most effective tool in credit risk evaluation. However, the performance of SVM is sensitive not only to the algorithm for solving the quadratic programming but also to the parameters setting in its learning machines as well as to the importance of different classes. In order to solve these issues, this paper proposes a weighted least squares support vector machine (LSSVM) classifier with design of experiment (DOE) for parameter selection for credit risk evaluation. In this approach, least squares algorithm is used to solve the quadratic programming, the DOE is used for parameter selection in SVM modelling and weights in LSSVM are used to emphasize the importance of difference classes. For illustration purpose, two publicly available credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed weighted LSSVM classifier. The results show that the proposed weighted LSSVM classifier with DOE can produce the promising classification results in credit risk evaluation, relative to other classifiers listed in this study. (C) 2011 Elsevier Ltd. All rights reserved.
关键词	Credit risk evaluation Weighted LSSVM classifier Least squares algorithm Design of experiment Parameter selection
DOI	10.1016/j.eswa.2011.06.023
语种	英语
资助项目	National Science Fund for Distinguished Young Scholars (NSFC)[71025005] ; National Natural Science Foundation of China (NSFC)[90924024] ; Chinese Academy of Sciences ; Hangzhou Key Laboratory of E-Business and Information Security, Hangzhou Normal University ; K.C. Wong Education Foundation, Hong Kong
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000295193400110
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/11717
专题	系统科学研究所
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, MADIS, Beijing 100190, Peoples R China 2.Hangzhou Normal Univ, Hangzhou Key Lab E Business & Informat Secur, Hangzhou 310036, Zhejiang, Peoples R China 3.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Yao, Xiao,Wang, Shouyang,et al. Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection[J]. EXPERT SYSTEMS WITH APPLICATIONS,2011,38(12):15392-15399.
APA	Yu, Lean,Yao, Xiao,Wang, Shouyang,&Lai, K. K..(2011).Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection.EXPERT SYSTEMS WITH APPLICATIONS,38(12),15392-15399.
MLA	Yu, Lean,et al."Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection".EXPERT SYSTEMS WITH APPLICATIONS 38.12(2011):15392-15399.