Comparative study on class imbalance learning for credit scoring

被引：3

作者：

Yao, Ping ^{[1
]}

机构：

[1] Heilongjiang Inst Sci & Technol, Sch Econ & Management, Harbin 150027, Peoples R China

来源：

HIS 2009: 2009 NINTH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS, VOL 2, PROCEEDINGS | 2009年

关键词：

class imbalance learning; credit scoring; C4.5; SVM; Rough Set; weighted C4.5; weighted SVM; weight Rough Set;

D O I：

10.1109/HIS.2009.133

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper performs systematic comparative studies on weighted methods including weight C4.5, weighted SVM and weighted rough set with traditional C4.5, SVM and rough set for credit scoring. The experiments show that the weighted methods outperform to the traditional methods when the methods are sensitive to the class distribution.

引用

页码：105 / 107

页数：3

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