CUSTOMER CLASSIFICATION IN COMMERCIAL BANK BASED ON ROUGH SET THEORY AND FUZZY SUPPORT VECTOR MACHINE
In the analysis of customer classification, redundant variables in the samples spoil the performance of the SVM classifier and reduce the recognition accuracy. On the other hand, we usually cant label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly. In order to solve the problems mentioned above, this paper used rough sets(RS) as a preprocessor of SVM to select a subset of input variables and employ fuzzy support vector machinc(FSVM), proposed in previous papers, to treat every sample as both positive and negative classes, but with different memberships. Additionally, the proposed RS-FSVM with membership based on affinity is tested on two different datasets. Then we compared the accuracies of proposed RS-FSVM model with other three models. Especially, in application of the proposed method, training sets are selected by increasing proportion. Experimental results showed that the RS-SVM model performed the best classification accuracy and generalization, implying that the hybrid of RS with fuzzy SVM model can serve as a promising alternative for customer classification.
Customer Classification Rough Set Theory Fuzzy Support Vector Machine
JIAN-GUO ZHOU TAO BAI
School of Business Administration, North China Electric Power University, Baoding 071003, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1212-1217
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）