Credit risk assessment with least squares fuzzy support vector machines

被引：0
|
作者：
Yu, Lean [1 ]
Lai, Kin Keung
Wang, Shouyang
机构：
[1] Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
[2] City Univ Hong Kong, Dept Management Sci, Hong Kong, Hong Kong, Peoples R China
[3] Hunan Univ, Coll Business Adm, Changsha, Peoples R China
来源：
ICDM 2006: Sixth IEEE International Conference on Data Mining, Workshops | 2006年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this study, we discuss a least squares version of fuzzy support vector machine (FSVM) classifiers for designing a credit risk assessment system to discriminate good creditors from bad ones. Relative to the classical FSVM, the least squares FSVM (LS-FSVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a real-world credit dataset is used to test the effectiveness of the LS-FSVM.
引用
收藏
页码：823 / 827
页数：5
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