dc.contributor.author | Saitta, Sandro | |
dc.contributor.author | Kripakaran, Prakash | |
dc.contributor.author | Raphael, Benny | |
dc.contributor.author | Smith, Ian F.C. | |
dc.date.accessioned | 2015-06-25T15:07:48Z | |
dc.date.issued | 2009-03-11 | |
dc.description.abstract | 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. | en_GB |
dc.description.sponsorship | Swiss National Science Foundation | en_GB |
dc.identifier.citation | Vol. 24 (1), pp. 3 - 10 | en_GB |
dc.identifier.doi | 10.1061/(ASCE)CP.1943-5487.0000003 | |
dc.identifier.grantnumber | NSF-CH200020-117670/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/17670 | |
dc.language.iso | en | en_GB |
dc.publisher | American Society of Civil Engineers | en_GB |
dc.rights | Copyright 2010 ASCE | en_GB |
dc.subject | Decision support | en_GB |
dc.subject | Feature selection | en_GB |
dc.subject | Global search | en_GB |
dc.subject | Indentification | en_GB |
dc.subject | Support vector machine | en_GB |
dc.subject | Wrapper | en_GB |
dc.title | Feature selection using stochastic search: An application to system identification | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2015-06-25T15:07:48Z | |
dc.identifier.issn | 0887-3801 | |
dc.identifier.journal | Journal of Computing in Civil Engineering | en_GB |