In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification...In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy展开更多
Traditional credit evaluation models failed to produce partial results due to their ignorance of the whole risks of credit environment. An excellent evaluating model on credit should take into account the credit envir...Traditional credit evaluation models failed to produce partial results due to their ignorance of the whole risks of credit environment. An excellent evaluating model on credit should take into account the credit environment impersonally and comprehensively. In this paper, a novel area＇s macroscopical credit evaluation model based on Fuzzy Neural Network is constructed. A set of scientific and reasonable evaluating indexes are extracted from feature space of macroscopical credit, then based on these indexes a Fuzzy Neural Network （FNN） model on credit evaluation is constructed and applied into the practical credit evaluation of some Chinese provinces randomly selected for the first time. Results show our model is both practical and capable.展开更多
文摘In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy
基金The research is supported by the Major Programs of Institute of Finance in Jinan University which is the Major Base of Social Science in Guangdong＇s Universities （04jdxm79001）, the Research Program of Innovative Team of Jinan University （04sk2d03）, National Natural Science Foundation of China（60574069） and the Soft Science Foundation of Guangdong Province （2005870101044）
文摘Traditional credit evaluation models failed to produce partial results due to their ignorance of the whole risks of credit environment. An excellent evaluating model on credit should take into account the credit environment impersonally and comprehensively. In this paper, a novel area＇s macroscopical credit evaluation model based on Fuzzy Neural Network is constructed. A set of scientific and reasonable evaluating indexes are extracted from feature space of macroscopical credit, then based on these indexes a Fuzzy Neural Network （FNN） model on credit evaluation is constructed and applied into the practical credit evaluation of some Chinese provinces randomly selected for the first time. Results show our model is both practical and capable.
