A Fixed Margin Based Proximal Ordinal Support Vector Regression Model

Abstract

Abstract This study extends the proximal support vector machine (PSVM) to the ordinal regression problems by proposing a proximal ordinal support vector regression (POSVR) based on the fixed margin strategy. The proposed POSVR is applied to the multi-class classification problems with ordered discrete outcome events under the assumption that the ordered classes can be separated by a group of parallel decision hyperplanes. POSVR can be transformed into a linear equation system and solved conveniently. The experimental results on the benchmark datasets and a real-world recovery rate dataset of US corporate bonds demonstrate that POSVR achieves better performance in terms of cross-validation outputs on the training set and out-of-sample prediction on the testing set. It implies that the proposed POSVR is a promising technique for credit risk modeling and other areas in ordinal regression problems.