Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy

  • Jaume Bacardit
  • David E. Goldberg
  • Martin V. Butz
  • Xavier Llorà
  • Josep M. Garrell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

Windowing methods are useful techniques to reduce the computational cost of Pittsburgh-style genetic-based machine learning techniques. If used properly, they additionally can be used to improve the classification accuracy of the system. In this paper we develop a theoretical framework for a windowing scheme called ILAS, developed previously by the authors. The framework allows us to approximate the degree of windowing we can apply to a given dataset as well as the gain in run-time. The framework sets the first stage for the development of a larger methodology with several types of learning strategies in which we can apply ILAS, such as maximizing the learning performance of the system, or achieving the maximum run-time reduction without significant accuracy loss.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • David E. Goldberg
    • 2
  • Martin V. Butz
    • 2
  • Xavier Llorà
    • 2
  • Josep M. Garrell
    • 1
  1. 1.Intelligent Systems Research GroupUniversitat Ramon LlullBarcelona, CataloniaSpain
  2. 2.Illinois Genetic Algorithms Laboratory (IlliGAL), Department of General EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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