文章基本信息

标题：Twin Support Vector Machines Based on Quantum Particle Swarm Optimization
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作者：Ding, Shifei ; Wu, Fulin ; Nie, Ru 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2013
卷号：8
期号：7
页码：1743-1750
DOI：10.4304/jsw.8.7.1743-1750
语种：English
出版社：Academy Publisher
摘要：Twin Support Vector Machines (TWSVM) are developed on the basis of Proximal Support Vector Machines (PSVM) and Proximal Support Vector Machine based on the generalized eigenvalues(GEPSVM). The solving of binary classification problem is converted to the solving of two smaller quadratic programming problems by TWSVM. And then it gets two non-parallel hyperplanes. Its efficiency of dealing with the problems and performance are better than the traditional support vector machines. However, it also has some problems. Its own parameters are difficult to be appointed. In order to solve this problem, on the basis of in-depth study of TWSVM, this paper proposes an algorithm that is the Twin Support Vector Machines based on Quantum Particle Swarm Optimization (QPSO-TWSVM) .By the use of the global searching ability of the Quantum Particle Swarm Optimization (QPSO), QPSO-TWSVM can search the optimal parameters in the global scope and avoid itself falling into the local optimum prematurely to find the values of the parameters which are the closest to the optimal parameters. QPSO-TWSVM avoids using the empirical values to appoint the parameters successfully. Compared with the traditional TWSVM, QPSO-TWSVM can appoint the parameters more accurately and avoid selecting the parameters blindly. Because of the better parameter selections, QPSO-TWSVM improves the classification accuracy of TWSVM.
关键词：QPSO;TWSVM;parameter optimization;binary classification