Structural regularized twin support vector machine based on within-class scatter and between-class scatter

摘要

Robust minimum class variance twin support vector machine(RMCV-TWSVM) presented previously gets better classification performance than the classical TWSVM. The RMCV-TWSVM introduces the class variance matrix of positive and negative samples into the construction of two hyperplanes. However, it does not consider the total structure information of all the samples, which can substantially reduce its classification accuracy. In this paper, a new algorithm named structural regularized TWSVM based on within-class scatter and between-class scatter(WSBS-STWSVM) is put forward. The WSBS-STWSVM can make full use of the total within-class distribution information and between-class structure information of all the samples. The experimental results illustrate high classification accuracy and strong generalization ability of the proposed algorithm.