基于模糊积分的集成支持向量机的商业银行信用风险评估

commercial bank credit risk assessment model based on Integrated Support Vector Machine (ISVM) which based on fuzzy integral is established through using the credit assessment index system. Since SVM can capture geometric characteristics of feature space and capable of extracting the optimal solution with the small training set size, the classifier of ISVM approach outperforms voting SVM, single SVM, fuzzy nerve network to the problem of commercial bank credit risk assessment. This model has nine input factors and one output factor. The effect of the variability in performance with respect to various values of parameters in ISVM is also examined. The results demonstrate that the accuracy and generalization performance of ISVM is better than others approach.