The Building of the Combined Model for Personal Credit Rating ——A Study Based on the Decision Tree-Neural Network
Cheng Chen · Jinrong luntan · 2013
The real customers’credit data of a German commercial bank as samples, this paper applies decision-tree method in the selection process of personal credit indicators. The decision tree method is combined with the BP (Back Propagation) neural network model to form a two-phase composite model. The study of the paper shows that, for the test samples, the classification prediction of the combined model, used for personal credit rating and based on decision tree-neural network, is more accurate than that of the single BP neural network model and the average of total correct rates is 75.45%, nearly 3 percentage higher than the single BP neural network model. The indicator selection of optimal decision tree, based on the information-entropy-gain classification principle, can rationally eliminate the interference of unimportant attribute indicators, indroduce the truly effective attribute indicators to enter the main model of the neural network and improve the accuracy of the classification prediction of the model.