Credit scoring using the clustered support vector machine

被引:217
作者
Harris, Terry [1 ]
机构
[1] Univ W Indies, Dept Management Studies, Credit Res Unit, Wanstead, Barbados
关键词
Credit risk; Credit scoring; Clustered support vector machine; Support vector machine; DISCRIMINANT-ANALYSIS; NEURAL-NETWORKS; RISK-ASSESSMENT; MODELS;
D O I
10.1016/j.eswa.2014.08.029
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:741 / 750
页数:10
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