Twin support vector machines based on rough sets

被引：0

作者：

Yu, Junzhao ^{[1
]}

Ding, Shifei ^{[1
,2
,3
]}

Jin, Fengxiang ^{[4
]}

Huang, Huajuan ^{[1
]}

Han, Youzhen ^{[1
]}

机构：

[1] School of Computer Science and Technology, China University of Mining and Technology, Xuzhou

[2] Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science

[3] Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications

[4] Geomatics College, Shandong University of Science and Technology

来源：

International Journal of Digital Content Technology and its Applications | 2012年 / 6卷 / 20期

关键词：

Attribution reduction; Data preprocessing; Rough sets; Twin support vector machines;

D O I：

10.4156/jdcta.vol6.issue20.53

中图分类号：

学科分类号：

摘要：

TWSVM(Twin Support Vector Machines) is based on the idea of GEPSVM (Proximal SVM based on Generalized Eigenvalues), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. In order to further improve the speed and accuracy of TWSVM, this paper proposes the twin support vector machines based on rough sets. Firstly, using the rough sets theory to reduce the attributes, and then using TWSVM to train and predict the new datasets. The final experimental results and data analysis show that the proposed algorithm has higher accuracy and better efficiency compared with the traditional twin support vector machines.

引用

页码：493 / 500

页数：7

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