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Cellular GEP-Induced Classifiers

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Book cover Computational Collective Intelligence. Technologies and Applications (ICCCI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6421))

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Abstract

In this paper we propose integrating two collective computational intelligence techniques gene expression programming and cellular evolutionary algorithms with a view to induce expression trees, which, subsequently, serve as weak classifiers. From these classifiers stronger ensemble classifiers are constructed using majority-voting and boosting techniques. The paper includes the discussion of the validating experiment result confirming high quality of the proposed ensemble classifiers.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Gdańsk University, Wita Stwosza 57, 80-952, Gdańsk, Poland

    Joanna Jędrzejowicz

  2. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

Authors
  1. Joanna Jędrzejowicz
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  2. Piotr Jędrzejowicz
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, 807, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, #43, Sec.4, Keelung Rd., 106, Taipei, Taiwan

    Shyi-Ming Chen

  3. Institute of Informatics, Wroclaw University of Technology, Str. Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Jędrzejowicz, J., Jędrzejowicz, P. (2010). Cellular GEP-Induced Classifiers. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16693-8_36

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  • DOI: https://doi.org/10.1007/978-3-642-16693-8_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16692-1

  • Online ISBN: 978-3-642-16693-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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