Abstract:Support Vector Machine (SVM) has been widely used in the field of credit evaluation as non-parametric method. However, it cannot actively select attributes when processing high-dimensional data which may cause a drop in accuracy. In order to overcome this shortcoming, credit evaluation model of C4.5 decision tree optimized SVM is constructed to select attributes, and reduce redundant attributes. The model determines the optimal parameters through grid search, uses F-score and average accuracy to evaluate model performance on two sets of public data sets. Empirical analysis shows that the proposed model effectively reduces data learning process, and has higher classification accuracy and practicability than the various traditional types of single models.