Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm

Abstract

The efficiency and performance of Twin Support Vector Machines (TWSVM) is better than the traditional support vector machines when it deals with the problems. However, it also has some problems. As the same as the traditional support vector machines, its parameters are difficult to be appointed and it is not easy to select the appropriate kernel function. TWSVM generally selects the Gaussian radial basis kernel function. Although its learning ability is very strong, its generalization ability is relatively weak. To a certain extent, this limits the performance of TWSVM .In order to solve these two problems, in this paper, we propose the Mixed Kernel Twin Support Vector Machines based on the shuffled frog leaping algorithm (SFLA-MK-TWSVM). To make full use of both the excellent generalization ability of global kernel function s and the learning ability of local kernel function s , SFLA-MK-TWSVM constructs a mixed kernel which has better performance. Then SFLA-MK-TWSVM uses the shuffled frog leaping algorithm to determine the parameters of both TWSVM and the mixed kernel function to further improve the performance of TWSVM. The experimental results indicate that SFLA-MK-TWSVM significantly improves the classification accuracy of TWSVM.