Support Vector Machine for Credit System: The Effect of Parameter Optimization and Training Sample

Abstract

The credit assessment system is an essential part of modern financial institutions, and most of them have adopted different models to perform the task according to their specific needs. Support Vector Machine has been widespread and proved an efficient classifier, especially for relatively small datasets in recent years. When using SVM, data processing, choosing an appropriate kernel function, and tuning parameters can largely affect its performance. The most popular kernel function of SVM is the Radial basis function (RBF), and its main parameters are the regularization parameter, C, and the kernel coefficient, . Our study based on the South German credit dataset demonstrates that parameter optimization and an appropriate ratio of the size of the training dataset to the size of the testing dataset could significantly improve the performance of SVM.