Feature Selection Using Stochastic Search: An Application to System Identification

System identification using multiple-model strategies may involve thousands of models with several parameters. However, only a few models are close to the correct model. A key task involves finding which parameters are important for explaining candidate models. The application of feature selection to system identification is studied in this paper. A new feature selection algorithm is proposed. It is based on the wrapper approach and combines two algorithms. The search is performed using stochastic sampling and the classification uses a support vector machine strategy. This approach is found to be better than genetic algorithm-based strategies for feature selection on several benchmark data sets. Applied to system identification, the algorithm supports subsequent decision making.

  • Availability:
  • Authors:
    • Saitta, Sandro
    • Kripakaran, Prakash
    • Raphael, Benny
    • Smith, Ian F C
  • Publication Date: 2010-1


  • English

Media Info

  • Media Type: Print
  • Features: Illustrations; References; Tables;
  • Pagination: pp 3-10
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01150925
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 31 2010 2:51PM