Stationary Mahalanobis Kernel SVM for Credit Risk Evaluation

This paper proposed Mahalanobis distance induced kernels in Support Vector Machines (SVMs) with applications in credit risk evaluation. We take a particular interest in stationary ones. Compared to traditional stationary kernels, Mahalanobis kernels take into account on feature's correlation and can provide a more suitable description on the behavior of the data sets. Results on real world credit data sets show that stationary kernels with Mahalanobis distance outperform the stationary kernels with various distance measures and they can also compete with frequently used kernels in SVM. The superior performance of our proposed kernels over other classical machine learning methods and the successful application of the kernels in large scale credit risk evaluation problems may imply that we have proposed a new class of kernels appropriate for credit risk evaluations.