Five-category classification of loan risk based on integration of rough sets and neural network
基于粗糙集和神经网络集成的贷款风险5级分类

An integrated model of rough set and neural network for five-category classification of loan risk is proposed. The financial data are discretized by using the self-organizing mapping neural network;and the evaluation indices are reduced without information loss through a genetic algorithm.The reduced indices are used to develop the rules for the five-category classification of loan risk,and to train the neural network.The rough set theory is used to determine the category for the test sample which matches all rules in the rule-base.The neural network is applied to separate those test samples which do not match any one rule in the rule-base.698 loan finns of five-category are selected as test samples. The prediction accuracy of the integrated model combining rough sets and neural network is 82.07%.This verifies the effectiveness of our approach.