Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  3. 3.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature Extraction, Foundations and Applications. Series Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hart, W.E., Krasnogor, N., Smith, J.: Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  5. 5.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)CrossRefzbMATHGoogle Scholar
  6. 6.
    Liu, H., Motoda, H.: Computational methods of feature selection. Chapman and Hall/CRC Editions (2008)Google Scholar
  7. 7.
    Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recogn. Lett. 10(5), 335–347 (1989)CrossRefzbMATHGoogle Scholar
  8. 8.
    Yusta, S.C.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recognition Letters 30(5), 525–534 (2009)CrossRefGoogle Scholar

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

Personalised recommendations