Hybrid Classifier Using Neighborhood Rough Set and SVM for Credit Scoring
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicants credit score from the applicants input features. (1) using neighborhood rough set to select input features, (2) using grid search to optimize RBF kernel parameters, (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation, (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability in comparing with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.
credit socring neighborhood rough set SVM hybrid classifier
Ping Yao
School of Economics & Management, Heilongjiang Institute of Science and Technology, Harbin, 150027, China
国际会议
北京
英文
138-142
2009-07-24（万方平台首次上网日期，不代表论文的发表时间）