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Journal of Information Science and Engineering, Vol. 26 No. 5, pp. 1695-1706 (September 2010)

Particle Swarm Optimization for Semi-supervised Support Vector Machine*

QING WU1,+, SAN-YANG LIU+ AND LE-YOU ZHANG+
 1School of Automation
Xi'an Institute of Posts and Telecommunications
Xi'an, Shaanxi, 710121 P.R. China
+Department of Mathematical Sciences
Xidian University
Xi'an, Shaanxi, 710071 P.R. China

Semi-supervised Support vector machine has become an increasingly popular tool for machine learning due to its wide applicability. Unlike SVM, their formulation leads to a non-smooth non-convex optimization problem. In 2005, Chapelle and Zien used a Gaussian approximation as a smooth function and presented TSVM. In this paper, we propose a smooth piecewise function and research smooth piecewise semi-supervised support vector machine (SPS3VM). The approximation performance of the smooth piecewise function is better than the Gaussian approximation function. According to the non-convex character of SPS3VM, a converging linear particle swarm optimization is first used to train semi-supervised support vector machine. Experimental results illustrate that our proposed algorithm improves TSVM in terms of classification accuracy.

Keywords: semi-supervised support vector machine, Gaussian approximation, smooth piecewise function, approximation performance, particle swarm optimization

Full Text (¥þ¤åÀÉ) Retrieve PDF document (201009_08.pdf)

Received September 19, 2008; revised November 25, 2008; accepted February 11, 2009.
Communicated by Chin-Teng Lin.
* This work was supported by the Nature Science Foundation of China under Grant (No.60574075) and Natural Science Foundation of Shaanxi Province (No. 2010JQ8004).
+ Corresponding author: xidianwq@yahoo.com.cn.