Sparse discriminant twin support vector machine for binary classification

For a binary classification problem, twin support vector machine (TSVM) has a faster learning speed than support vector machine (SVM) by seeking a pair of nonparallel hyperplanes. However, TSVM has two deficiencies: poor discriminant ability and poor sparsity. To relieve them, we propose a novel sparse discriminant twin support vector machine (SD-TSVM). Inspired by the idea of the Fisher criterion, maximizing the between-class scatter and minimizing the within-class scatter, SD-TSVM introduces twin Fisher regularization terms, which may improve the discriminant ability of SD-TSVM. Moreover, SD-TSVM has a good sparsity by utilizing both the 1-norm of model coefficients and the hinge loss. Thus, SD-TSVM can efficiently perform data reduction. Classification results on nine real-world datasets show that SD-TSVM has a satisfactory performance compared with related methods.