Neighborhood rough set and SVM based hybrid credit scoring classifier

Authors:
Yao Ping;Lu Yongheng
Affiliations:
College of Economics & Management, Heilongjiang Institute of Science and Technology, Harbin 150027, China;College of Economics & Management, Heilongjiang Institute of Science and Technology, Harbin 150027, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 7
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Abstract

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant's credit score according to the applicant's 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 compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.