A New Credit Scoring Method Based on Rough Sets and Decision Tree

Credit scoring is a very typical classification problem in Data Mining. Many classification methods have been presented in the literatures to tackle this problem. The decision tree method is a particularly effective method to build a classifier from the sample data. Decision tree classification method has higher prediction accuracy for the problems of classification, and can automatically generate classification rules. However, the original sample data sets used to generate the decision tree classification model often contain many noise or redundant data. These data will have a great impact on the prediction accuracy of the classifier. Therefore, it is necessary and very important to preprocess the original sample data. On this issue, a very effective approach is the rough sets. In rough sets theory, a basic problem that can be tackled using rough sets approach is reduction of redundant attributes. This paper presents a new credit scoring approach based on combination of rough sets theory and decision tree theory. The results of this study indicate that the process of reduction of attribute is very effective and our approach has good performance in terms of prediction accuracy.