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Feature selection for ensembles using Non-dominated Sorting in Genetic Algorithms

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2 Author(s)
You Ji ; Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China ; Shiliang Sun

Feature selection for ensembles can often improve generalization accuracy of classifiers. In this paper we present a strategy on the feature selection for ensembles based on a hierarchical Non-dominated Sorting in Genetic Algorithms (NSGA-II) proposed by Deb. The first level of our strategy performs feature selection in order to generate a set of good classifiers, the second one deletes redundant classifiers while the third one combines classifiers left to provide a series of powerful ensembles. The proposed strategy is evaluated on data sets of UCI, using support vector machine as our classifiers. Our experiments demonstrated the effectiveness of our strategy.

Published in:

Natural Computation (ICNC), 2010 Sixth International Conference on  (Volume:2 )

Date of Conference:

10-12 Aug. 2010

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