Discriminative information-based nonparallel support vector machine

Abstract Twin support vector machine (TSVM), as a typical nonparallel support vector machine, has been demonstrated to be effective in terms of classification performance. However, the existing TSVM does not take the within-class and between-class constraint projections into account. Inspired by modified pairwise constraint trick, we propose a novel classifier termed discriminative information-based nonparallel support vector machine (DINPSVM) to improve the performance of TSVM by introducing two novel regularization terms for each hyperplane, which takes the tightness between the similar patterns and discrepancy between the dissimilar pairs into consideration. The new classifier can not only learn the prior discriminative information about each constrained pair, but also combine the discrimination metric and spatial distance measure together. Experimental results on an artificial and twenty-three UCI datasets verify the efficiency and advantage of the proposed DINPSVM.