Comparative study on class imbalance learning for credit scoring

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.

关键词： class imbalance learning credit scoring C4.5 SVM Rough Set weighted C4.5 weighted SVM weight Rough Set

作者: Ping Yao

作者单位: School of Economics & Management, Heilongjiang Institute of Science and Technology, Harbin, 150027, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-3

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）