Hybrid Classifier Using Neighborhood Rough Set and SVM for Credit Scoring

Abstract

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 applicantpsilas credit score from the applicantpsilas 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.