Research of Housing Loan Credit Evaluation Based SVM

Credit risk is the primary source of risk to financial institutions. Support vector machine (SVM) is a good classifier to solve binary classification problem and the learning results possess stronger robustness. The attribute reduction of rough set has been applied as preprocessor, then resolving the problem of the application of SVM in housing loan credit evaluation, such as the choice of kernel function and parameters, the problem of unbalance data. The experiment show: Gausslan model is suitable better for practical application, grid-search method adjusts these penalty parameters to achieve better generalization performances in the application, respectively penalty SVM(RP_SVM) can resolve unbalance problem efficiency.