关键词: v-支持向量机, 粗糙集, 边界样本集, 支持向量

Abstract:

V-support vector machine (v-SVM) can take up a lot of training time when largescale samples set. V-support vector machine hybrid classification algorithm based on boundary of rough set (RSBv-SVM) is proposed. According to the merits of boundary region of rough set theory, the algorithm gets the boundary set of the classified data, which includes all support vectors. The boundary set can substitute the original inputs as a training subset, and the size of the training set is decreased. Training time is reduced by v-SVM while keeping the accuracy of classification and the performance of generalization. The simulation experiments show the effectiveness of the suggested hybrid method.

Key words: v-support vector machine, rough set, boundary set, support vectors