Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (3): 564-572.doi: 10.21629/JSEE.2019.03.14

• Systems Engineering • Previous Articles     Next Articles

Construction and application of pre-classified smooth semi-supervised twin support vector machine

Xiaodan ZHANG*(), Hongye QI()   

  • Received:2018-05-04 Online:2019-06-01 Published:2019-07-04
  • Contact: Xiaodan ZHANG E-mail:bkdzxd@163.com;1614041218@qq.com
  • About author:ZHANG Xiaodan was born in 1959. She is a professor of mathematics in School of Mathematics and Physics, University of Science and Technology Beijing. In 2009, she was a visiting professor at DIMACS, Rutgers University, USA. Her research interests include data mining and dynamical systems. E-mail:bkdzxd@163.com|QI Hongye was born in 1992. She received her B.S. degree in 2016, and M.S. degree in mathematics from the University of Science and Technology Beijing in 2018. She is a system designer at Space Star Technology Co., LTD. Her research interests is data mining. E-mail:1614041218@qq.com
  • Supported by:
    the Fundamental Research Funds for University of Science and Technology Beijing(FRF-BR-12-021);This work was supported by the Fundamental Research Funds for University of Science and Technology Beijing (FRF-BR-12-021)

Abstract:

In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabeled samples. In S2TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S2TSVM (PS2TSVM) is proposed. Compared with S2TSVM, PS2TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS2TSVM is smoothed. After smoothing the model, the pre-classified smooth S2TSVM (PS3TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3TSVM model has better classification results.

Key words: semi-supervised, twin support vector machine (TWSVM), pre-classified, center-distance, smooth