Evolving Classifiers Ensembles with Heterogeneous Predictors

  • Pier Luca Lanzi
  • Daniele Loiacono
  • Matteo Zanini
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pier Luca Lanzi
    • 1
    • 2
  • Daniele Loiacono
    • 1
  • Matteo Zanini
    • 1
  1. 1.Artificial Intelligence and Robotics Laboratory (AIRLab)Politecnico di MilanoMilanoItaly
  2. 2.Illinois Genetic Algorithm Laboratory (IlliGAL)University of Illinois at Urbana ChampaignUrbanaUSA

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