Support vector machines for credit scoring and discovery of significant features

被引：167
|
作者：
Bellotti, Tony [1 ]
Crook, Jonathan [1 ]
机构：
[1] Univ Edinburgh, Management Sch & Econ, Credit Res Ctr, Edinburgh EH8 9JY, Midlothian, Scotland
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期
基金：
英国工程与自然科学研究理事会;
关键词：
SVM; Credit scoring; Feature selection; CLASSIFIER TECHNOLOGY; ILLUSION; PROGRESS;
D O I：
10.1016/j.eswa.2008.01.005
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The assessment of risk of default oil credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：3302 / 3308
页数：7
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