Combining multiple classifiers based on Dempster-Shafer theory for personal credit scoring

With the rapid development of China´s consumer credit market, personal credit scoring has attracted more and more attention. And a variety of statistical and artificial intelligence methods have been used in personal credit scoring. This paper presents a multiple classifier fusion method, namely using relevant theory and methods to combine single classifiers. This is for making full use of the complementary information between classifiers, and ultimately achieves the purpose of better classification results. We present an investigation into the fusion of two different classification methods for personal credit scoring, using Dempster-Shafer´s rule of fusion. These methods include traditional linear Logistic Regression and newly developing nonlinear BP neural network. Our experiment´s results show that the performance of the fusion of these two different classifiers on real consumer credit customer data is good. Its classification accuracy is better than that of the individual method and its type II error rate is effectively reduced, taking the advantage of the fusion of two classifiers. And it exerts a very important significance on the control of the risk of commercial banks´ business.