JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2010, Vol. 40 ›› Issue (5): 60-65.

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A fast SVM-based feature selection method

DAI Ping, LI Ning*   

  1. National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  • Received:2010-04-02 Online:2010-10-16 Published:2010-04-02

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Abstract:

Aiming at the large computation and slow convergence speed of the traditional feature selection methods, a fast SVMbased feature selection method is proposed to overcome.Support vecor machine is employed as the classifier and particle swarm optimization method is employed as searching strategy.The proposed method reduces the iterations of training classifiers by taking advantage of the characteristics of linear and polynomial kernel functions so that it reduces the complexity of calculation. Experimental results show that the method accelerates feature selection in the case of no loss of classification performances.

Key words:  feature selection, support vector machine, particle swarm optimization

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