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A memetic algorithm with support vector machine for feature selection and classification

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Abstract

The memetic algorithm (MA) is an evolutionary metaheuristic that can be viewed as a hybrid genetic algorithm combined with some kinds of local search. In this paper, we propose a memetic algorithm combined with a support vector machine (SVM) for feature selection and classification in Data mining. The proposed approach tries to find a subset of features that maximizes the classification accuracy rate of SVM. In addition, another hybrid algorithm of MA and SVM with optimized parameters is also developed. The two versions of our proposed method are evaluated on some datasets and compared with some well-known classifiers for data classification. The computational experiments show that the hybrid method MA + SVM with optimized parameters provides competitive results and finds high quality solutions.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. They would like also to thank the developers of Waikato Environment for Knowledge Analysis (WEKA) and the Library for Support Vector Machines (LIBSVM) for the provision of the open source code.

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Authors and Affiliations

  1. LIMOSE Laboratory, Computer Science Department, Faculty of Sciences, University M’Hamed Bougara of Boumerdès, Independency Avenue, Boumerdès, 35000, Algeria

    Messaouda Nekkaa

  2. LRIA/Computer Sciences Department, University of Sciences and Technology Houari Boumediene (USTHB), BP 32, El-Alia, Beb-Ezzoaur, 16111, Algiers, Algeria

    Dalila Boughaci

Authors
  1. Messaouda Nekkaa
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  2. Dalila Boughaci
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Corresponding author

Correspondence to Dalila Boughaci.

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Special Issue on Hybrid Nature Inspired Algorithm: Concept, Analysis and Application.

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Nekkaa, M., Boughaci, D. A memetic algorithm with support vector machine for feature selection and classification. Memetic Comp. 7, 59–73 (2015). https://doi.org/10.1007/s12293-015-0153-2

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  • DOI: https://doi.org/10.1007/s12293-015-0153-2

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