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Credit scoring using support vector machines with direct search for parameters selection
Zhou, Ligang1; Lai, Kin Keung1; Yu, Lean1,2
2009
Source PublicationSOFT COMPUTING
ISSN1432-7643
Volume13Issue:2Pages:149-155
AbstractSupport 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.
KeywordCredit scoring Direct search Support vector machines Genetic algorithm
DOI10.1007/s00500-008-0305-0
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000260518100007
PublisherSPRINGER
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/7970
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLai, Kin Keung
Affiliation1.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
Recommended Citation
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.
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