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.


Published in:
Journal of Computing in Civil Engineering, 24, 1, 3-10
Year:
2010
Publisher:
American Society of Civil Engineers
ISSN:
0887-3801
Keywords:
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 Record created 2009-02-23, last modified 2018-09-13

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