Feature Selection in Credit Scoring Model for Credit Card Applicants in XYZ Bank: A Comparative Study

Abstract

The performance of credit scoring models is determined by the used features. The relevant features for credit scoring usually are determined unsystematic and dominate by arbitrary trial. This paper presents a comparative study of four feature selection methods, which use data mining approach in reducing the feature space. The final results show that among the four feature selection methods, the Gini Index and Information Gain algorithms perform better than others with the classification accuracy of 75.46% and 75.44% respectively.