Enterprise credit risk evaluation research based on improved FSVM model

被引：0
|
作者：
Xiao, Yihong [1 ]
Liu, Ming [1 ]
Cao, Mengyun [1 ]
机构：
[1] Department of Management Science and Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China
来源：
ICIC Express Letters, Part B: Applications | 2014年 / 5卷 / 05期
关键词：
Classification accuracy - Comparative experiments - Credit risk evaluation - Credit risk management - Credit risks - FSVM - Fuzzy membership - Fuzzy support vector machines;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Credit risk evaluation is a base work of credit risk management, which can be accomplished by means of FSVM (Fuzzy Support Vector Machine). However, the classification accuracy of FSVM is not very well. To enhance the classification accuracy, we propose an FSVM model which is improved by introducing a new fuzzy membership. The new fuzzy membership takes opposite membership into consideration to reduce the impact from singular points. By comparative experiment, we found that the FSVM with new membership function can improve classification accuracy in enterprise credit risk evaluation, so can help commercial banks lower the credit risk. © 2014 ICIC International.
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页码：1321 / 1325
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