Credit scoring using support vector machines with direct search for parameters selection

doi:10.1007/s00500-008-0305-0

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	Credit scoring using support vector machines with direct search for parameters selection
	Zhou, Ligang 1; Lai, Kin Keung 1; Yu, Lean 1,2
	2009
发表期刊	SOFT COMPUTING
ISSN	1432-7643
卷号	13 期号:2 页码:149-155
摘要	Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters' setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.
关键词	Credit scoring Direct search Support vector machines Genetic algorithm
DOI	10.1007/s00500-008-0305-0
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000260518100007
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/7970
专题	中国科学院数学与系统科学研究院
通讯作者	Lai, Kin Keung
作者单位	1.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Ligang,Lai, Kin Keung,Yu, Lean. Credit scoring using support vector machines with direct search for parameters selection[J]. SOFT COMPUTING,2009,13(2):149-155.
APA	Zhou, Ligang,Lai, Kin Keung,&Yu, Lean.(2009).Credit scoring using support vector machines with direct search for parameters selection.SOFT COMPUTING,13(2),149-155.
MLA	Zhou, Ligang,et al."Credit scoring using support vector machines with direct search for parameters selection".SOFT COMPUTING 13.2(2009):149-155.