Comparing and Selecting SVM-Kernels for Credit Scoring

Abstract

Kernel methods for classification problems map data points into feature spaces where linear separation is performed. Detecting linear relations has been the focus of much research in statistics and machine learning, resulting in efficient algorithms that are well understood, with many applications including credit scoring problems. However, the choice of more appropriate kernel functions using nonlinear feature mapping may still improve this classification performance. We show, how different kernel functions contribute to the solution of a credit scoring problem and we also show how to select and compare such kernels.