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|Title:||Feature selection using stochastic search: An application to system identification|
Support vector machine
|Source:||Saitta, S., Kripakaran, P., Raphael, B., Smith, I.F.C. (2010). Feature selection using stochastic search: An application to system identification. Journal of Computing in Civil Engineering 24 (1) : 3-10. ScholarBank@NUS Repository. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000003|
|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. © 2010 ASCE.|
|Source Title:||Journal of Computing in Civil Engineering|
|Appears in Collections:||Staff Publications|
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