A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

被引：0
|
作者：
Liu, Jingli [1 ,2 ]
Li, Jianping [2 ]
Xu, Weixuan [2 ]
Shi, Yong [3 ]
机构：
[1] Univ Sci & Technol China, Sch Management, Hefei, Peoples R China
[2] Chinese Acad Sci, Inst Policy & Management, Beijing, Peoples R China
[3] Chinese Acad Sci, Data Technol & Knowledge Econ, Beijing, Peoples R China
来源：
CUTTING-EDGE RESEARCH TOPICS ON MULTIPLE CRITERIA DECISION MAKING, PROCEEDINGS | 2009年 / 35卷
基金：
中国国家自然科学基金;
关键词：
LS-SVM; Kernel principle component analysis; credit risk assessment;
D O I：
10.1007/978-3-642-02298-2_83
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful toot for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares Support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.
引用
收藏
页码：566 / +
页数：2
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