Saitta, S.Kripakaran, P.Raphael, B.Smith, I.F.C.2009-02-232009-02-232009-02-23201010.1061/(ASCE)CP.1943-5487.0000003https://infoscience.epfl.ch/handle/20.500.14299/35592WOS:000273037200002System 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.Feature selectionWrapperSupport vector machineGlobal searchIndentificationDecision supportSupport Vector MachinesAlgorithmstext::journal::journal article::research article