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Generalization in the XCSF classifier system: analysis, improvement, and extension.

Pier Luca Lanzi1, Daniele Loiacono, Stewart W Wilson

  • 1Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, I-20133, Italy. lanzi@elet.polimi.it

Evolutionary Computation
|May 31, 2007
PubMed
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This study enhances the eXternal Classifier System (XCSF) by improving classifier weight updates, leading to more efficient generalization. New methods, particularly least squares, significantly boost XCSF performance and robustness.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • The eXternal Classifier System (XCSF) is a learning system that evolves accurate and parsimonious models.
  • Generalization in XCSF can be influenced by input range and classifier weight convergence.
  • The Widrow-Hoff update rule in XCSF can lead to slow convergence, resulting in suboptimal approximations.

Purpose of the Study:

  • To analyze and improve generalization capabilities in the XCSF algorithm.
  • To address the issue of slow classifier weight convergence in XCSF.
  • To explore methods for enhancing XCSF's ability to evolve more efficient approximations.

Main Methods:

  • Theoretical analysis of classifier weight convergence in XCSF using eigenvalue properties.

Related Experiment Videos

  • Introduction of three novel methods for updating classifier weights: condition-based normalization, linear least squares, and recursive linear least squares.
  • Experimental evaluation of the proposed methods on XCSF performance and generalization.

    • Main Results:

    • Input range significantly influences the types of generalizations evolved by XCSF.
    • All three proposed methods improve XCSF generalization, with least squares approaches showing superior performance and robustness.
    • The system can be extended to incorporate polynomial approximations for enhanced modeling.

    Conclusions:

    • The proposed weight update methods effectively increase XCSF's generalization capabilities.
    • Least squares-based updates offer the most significant improvements in performance and robustness for XCSF.
    • XCSF can be extended to handle more complex relationships using polynomial approximations.