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Tournament Searching Method to Feature Selection Problem

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Artifical Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6114))

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Abstract

A new search method to the feature selection problem – the tournament searching – is proposed and compared with other popular feature selection methods. The tournament feature selection method is a simple stochastic searching method with only one parameter controlling the global-local searching properties of the algorithm. It is less complicated and easier to use than other stochastic methods, e.g. the simulated annealing or genetic algorithm. The algorithm was tested on several tasks of the feature selection in the supervised learning. For comparison the simulated annealing, genetic algorithm, random search and two deterministic methods were tested as well. The experiments showed the best results for the tournament feature selection method in relation to other tested methods.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-200, Czestochowa, Poland

    Grzegorz Dudek

Authors
  1. Grzegorz Dudek
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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© 2010 Springer-Verlag Berlin Heidelberg

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Dudek, G. (2010). Tournament Searching Method to Feature Selection Problem. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_53

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  • DOI: https://doi.org/10.1007/978-3-642-13232-2_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13231-5

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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