灰狼优化的混合参数多分类孪生支持向量机

孪生支持向量机(TWSVM)是在支持向量机(SVM)的基础上产生的一种高效二分类算法,由于现实中存在的问题大多数是多分类的,将二分类孪生支持向量机扩展到多分类孪生支持向量机(MTWSVM)是非常重要的。目前常用的MTWSVM一般是基于“一对一”策略,但该策略中各子分类器都采用相同的惩罚参数以及核参数,忽略了不同子分类器之间的差异,不能使其发挥最好的作用。通过提出一种基于混合参数的多分类孪生支持向量机(MP-MTWSVM),为不同的子分类器选取合适的参数,保持分类器的多样性,进而根据“一对一”策略构建MTWSVM。TWSVM本就面临着参数难确定的问题,而MP-MTWSVM算法又引入了大量的参数,通过灰狼算法(GWO)对MP-MTWSVM的参数进行寻优,进一步提出了基于灰狼优化的混合参数多分类孪生支持向量机(GWO-MP-MTWSVM)。通过实验表明,GWO可以快速找到各子分类器的最优参数,并进一步提升了算法的准确率。

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

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 subclassifier uses the same penalty parameters and core parameters,ignoring the differences between different subclassifiers,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 subclassifiers,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.