Parameter determination of support vector machine and feature selection using simulated annealing approach

  • Authors:
  • Shih-Wei Lin;Zne-Jung Lee;Shih-Chieh Chen;Tsung-Yuan Tseng

  • Affiliations:
  • Department of Information Management, Chang Gung University, No. 259 Wen-Hwa 1st Road, Kwei-Shan Tao-Yuan 333, Taiwan, ROC and Department of Information Management, Huafan University, No. 1 Huafan ...;Department of Information Management, Huafan University, No. 1 Huafan Road, Taipei, Taiwan, ROC;Department of Industrial Management, National Taiwan University of Science and Technology, No. 43 Keelung Road, Sec. 4, Taipei, Taiwan, ROC;Department of Information Management, Huafan University, No. 1 Huafan Road, Taipei, Taiwan, ROC

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2008

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Abstract

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.