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Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) pp 29–42Cite as

An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 512))

Abstract

The Feature Selection Problem is an interesting and important topic which is relevant for a variety of database applications. This paper applies a hybridized version of the Differential Evolution algorithm, the Island Memetic Differential Evolution algorithm, for solving the feature subset selection problem while the Nearest Neighbor Classification method is used for the classification task. The performance of the proposed algorithm is tested using various benchmark datasets from the UCI Machine Learning Repository. The algorithm is compared with variants of the differential evolution algorithm, a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm and with a number of algorithms from the literature.

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Author information

Authors and Affiliations

  1. Department of Production Engineering and Management, Technical University of Crete, Chania, Greece

    Magdalene Marinaki & Yannis Marinakis

Authors
  1. Magdalene Marinaki
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  2. Yannis Marinakis
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Correspondence to Magdalene Marinaki .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Nottingham, Nottingham, United Kingdom

    German Terrazas

  2. School of Computing, University of Kent, Canterbury, Kent, United Kingdom

    Fernando E. B. Otero

  3. Center for Research on ICT, University of Granada, Granada, Spain

    Antonio D. Masegosa

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Marinaki, M., Marinakis, Y. (2014). An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-01692-4_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01691-7

  • Online ISBN: 978-3-319-01692-4

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