工业工程

工业工程 ›› 2012, Vol. 15 ›› Issue (1): 93-98.

• 专题论述 • 上一篇    下一篇

基于双隶属模糊支持向量机的中小企业信用评价

  

  1. 北京航空航天大学 经济管理学院，北京 100191
  • 出版日期:2012-02-29 发布日期:2012-03-13
  • 作者简介:宋晓东（1984-），男，山东省人，讲师，博士，主要研究方向为数据挖掘、信用管理、公司治理.
  • 基金资助:

     国家自然科学基金资助项目（70831001；70821061）；北京市自然科学基金资助项目（9102013）

     

Credit Evaluation for SmallandMediumSized Enterprises Based on Fuzzy SVM with Dual Membership Values

  1. School of Economics and Management, Beihang University, Beijing 100191, China
  • Online:2012-02-29 Published:2012-03-13

摘要：  构建了中小企业信用评价的双隶属模糊支持向量机模型（DFSVM），使每个训练样本依双隶属度同时隶属于两个信用类别，并通过粗糙集的属性约简方法确定支持向量机的最优输入指标组合。考虑到银行对于信用风险的厌恶，在模型的训练阶段对样本进行了“非对称”处理。实证结果表明，与传统的判别分析方法相比，建立的企业信用判别模型精度更高，调整后的模型可以进一步降低银行的信用风险。  

关键词:  , 粗糙集, 支持向量机, 信用评价, 双隶属度

Abstract:  A new fuzzy support vector machine (SVM) model with dual membership values, called DFSVM in short, is developed for the credit evaluation of smallandmediumsized enterprises. In this model, each sample belongs to two credit classes according to its dual membership values. The optimal input indicator portfolios are determined by using the attribute reduction method in rough set theory. With banks credit risk aversions taken into account, samples in the two classes are handled asymmetrically in the training process. Empirical results show that the discrimination accuracy of the proposed DFSVM model is superior to the traditional discrimination models. Furthermore, an adjusted model is proposed, test shows that the adjusted DFSVM model can further reduce the banks credit risk.

Key words: rough set, support vector machine (SVM), credit evaluation, dual membership values