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International Workshop on Learning Classifier Systems

International Workshop on Learning Classifier Systems

IWLCS 2006, IWLCS 2007: Learning Classifier Systems pp 218–234Cite as

Evolving Classifiers Ensembles with Heterogeneous Predictors

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4998))

Abstract

XCS with computed prediction, namely XCSF, extends XCS by replacing the classifier prediction with a parametrized prediction function. Although several types of prediction functions have been introduced, so far XCSF models are still limited to evolving classifiers with the same prediction function. In this paper, we introduce XCSF with heterogeneous predictors, XCSFHP, which allows the evolution of classifiers with different types of prediction function within the same population. We compared XCSFHP to XCSF on several problems. Our results suggest that XCSFHP generally performs as XCSF with the most appropriate prediction function for the given problem. In particular, XCSFHP seems able to evolve, in each problem subspace, the most adequate type of prediction function.

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Author information

Authors and Affiliations

  1. Artificial Intelligence and Robotics Laboratory (AIRLab), Politecnico di Milano, P.za L. da Vinci 32, I-20133, Milano, Italy

    Pier Luca Lanzi, Daniele Loiacono & Matteo Zanini

  2. Illinois Genetic Algorithm Laboratory (IlliGAL), University of Illinois at Urbana Champaign, Urbana, IL 61801, USA

    Pier Luca Lanzi

Authors
  1. Pier Luca Lanzi
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  2. Daniele Loiacono
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  3. Matteo Zanini
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Editor information

Editors and Affiliations

  1. University of Nottingham, School of Computer Science, ASAP research group, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, Sutton Bonington, LE12 5RD, UK

    Jaume Bacardit

  2. Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2, Universitat Ramon Llull, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  3. Department of Psychology, University of Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

  4. Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Tim Kovacs

  5. Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL), University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, 61801-2996, Urbana, IL, USA

    Xavier Llorà

  6. Tokyo Institute of Technology, 152-8550, tokyo, Japan

    Keiki Takadama

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© 2008 Springer-Verlag Berlin Heidelberg

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Lanzi, P.L., Loiacono, D., Zanini, M. (2008). Evolving Classifiers Ensembles with Heterogeneous Predictors. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_13

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  • DOI: https://doi.org/10.1007/978-3-540-88138-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88137-7

  • Online ISBN: 978-3-540-88138-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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