关键词: 多分类孪生支持向量机（MTWSVM）, 一对一策略, 混合参数, 灰狼优化（GWO）

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.

Key words: multi-classification twin support vector machine (MTWSVM), one-versus-one strategy, mixed parameter, grey wolf optimizer (GWO)