Research of Bad Debt Risk Based on Rough sets and Binary Tree SVMMulti-layer Classifier
In this paper, a bad-debt-risk evaluationmodel is established based on rough sets and binary tree SVM multi-layer classifier. First, we use rough sets topre-process a new set of index system including typical 5Cfinancial indices system which combines both financial andnon-financial factors on the basis of the evaluation method. We define the bad debt rating as four classes- normality, attention, doubt and loss via analyzing accounts payable. Then, BP neural network is used to assess the 180 sampleswhich are stochastically extracted from listed companies, and the four classes are identified by the trained classifierusing 65 samples. Finally the binary SVM multi-layerclassifier is also used to compare the result with which from BP neural network. The test results show that the classifierhas an excellent performance on training accuracy andreliability. The experiment results indicate that multi-layer SVM classifier is effective in credit risk assessment andachieves better performance than BP neural network.
Bad Debt Risk Rough sets Binary Tree SVMMulti-layer Classifier
Jinyu Tian Jianhong Ma
North China Electric Power University
国际会议
北京
英文
910-915
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）