Applying Kernel Logistic Regression in data mining to classify credit risk

Abstract

Credit risk evaluation is an interesting and important data mining problem in financial analysis domain. This problem domain, do require estimable class probabilities as well as accurate classification method. One of classification methods in the kernel-machine techniques and data mining communities that allows non linear probabilistic classification, transparent reasoning, and competitive discriminative ability is Kernel Logistic Regression. Kernel Logistic Regression model is a kernelized version of Logistic Regression, which well known classification method in the field of statistical learning. The parameters of kernel model are given by the solution of a convex optimization problem, that can be found using the efficient Iteratively Re-weighted Least Squares (IRLS) algorithm. In this paper, we investigated the classification performance of applying Kernel Logistic Regression to classify risk credit problem. The result demonstrated that Kernel Logistic Regression has good accuracy to evaluate credit risk, comparable with another well known kernel machine, Support Vector Machine.