Grey Wolf Optimizes Mixed Parameter Multi-Classification Twin Support Vector Machine

Abstract

Twin support vector machine (TWSVM) is an efficient binary classification algorithm based on support vector machine (SVM). Since most of the problems in reality are multi-classified, it is very important to extend binary classification twin support vector machine to multi-classification twin support vector machine (MTWSVM). At present, the commonly used MTWSVM is generally based on the “one-versus-one” strategy, but each sub-classifier uses the same penalty parameters and core parameters, ignoring the differences between different sub-classifiers, so it cannot play its best role. This paper proposes a multi-classification twin support vector machine based on mixed parameters (MP-MTWSVM). This algorithm selects appropriate parameters for different sub-classifiers, maintaining the diversity of classifiers, then constructing MTWSVM in terms of the “one-versus-one” strategy. TWSVM faces the problem that its parameters are difficult to be appointed, and MP-MTWSVM algorithm introduces a large number of parameters. This paper optimizes the parameters of MP-MTWSVM by using grey wolf optimizer algorithm (GWO), and further proposes a mixed parameter multi-classification twin support vector machine based on grey wolf optimizer (GWO-MP-MTWSVM). Experiments show that GWO can quickly find the optimal parameters of sub-classifiers and further improve the accuracy of the algorithm.

Keywords

• multi-classification twin support vector machine (mtwsvm)
• one-versus-one strategy
• mixed parameter
• grey wolf optimizer (gwo)