Memetic Feature Selection: Benchmarking Hybridization Schemata

  • M. A. Esseghir
  • Gilles Goncalves
  • Yahya Slimani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6076)


Feature subset selection is an important preprocessing and guiding step for classification. The combinatorial nature of the problem have made the use of evolutionary and heuristic methods indispensble for the exploration of high dimensional problem search spaces. In this paper, a set of hybridization schemata of genetic algorithm with local search are investigated through a memetic framework. Empirical study compares and discusses the effectiveness of the proposed local search procedure as well as their components.


Feature Selection Local Search Feature Subset Neighborhood Structure Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • M. A. Esseghir
    • 1
    • 2
  • Gilles Goncalves
    • 1
  • Yahya Slimani
    • 2
  1. 1.University of Lille Nord de France, F-59000, Lille, Artois University, LGI2A LaboratoryFrance
  2. 2.Sciences Faculty of TunisTunis El-Manar University

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