SVM-based Credit Rating and Feature Selection

被引：1

作者：

Qin, Yu-qiang ^{[1
,2
]}

Qi, Yu-dong ^{[1
]}

Ying, Hui ^{[3
]}

机构：

[1] Capital Univ Econ & Business, Coll Business Adm, Beijing 100070, Peoples R China

[2] Taiyuan Univ Sci & Technol, Coll Econ & Management, Taiyuan 030024, Shanxi, Peoples R China

[3] Taiyuan Normal Univ, Taiyuan, Peoples R China

来源：

MATERIALS, MACHINES AND DEVELOPMENT OF TECHNOLOGIES FOR INDUSTRIAL PRODUCTION | 2014年 / 618卷

关键词：

SVM; Credit rating; Feature selection; SUPPORT VECTOR MACHINES; CLASSIFICATION;

D O I：

10.4028/www.scientific.net/AMM.618.573

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit rating for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines(SVM) 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.

引用

页码：573 / +

页数：2