C5.0 classification algorithm and its application on individual credit score for banks
C5.0分类算法及在银行个人信用评级中的应用

This paper mainly focuses on individual credit evaluation of a commercial bank. The records of individual credit include numerical value as well as un-numerical values. Decision tree is a good solution for this kind of issue. Until now, research on the decision tree algorithm of C4.5 has become mature, but C5.0 algorithm is still not open because of commercial secret. This article does some detailed research into C5.0 algorithm and its related technology of "boosting". We also construct cost matrix and cost-sensitive tree, embed with boosting technology, and finally establish the individual credit evaluation model of Commercial Bank based on C5.0, which is used to evaluate the individual credit records of a German bank. Meanwhile, we compare the adjusted decision tree model and the original one. The simulation result shows that the evaluation result of the adjusted decision tree model is better.