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Design and Analysis of Learning Classifier Systems

A Probabilistic Approach

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  • © 2008

Overview

Authors:
  1. Jan Drugowitsch
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  • Latest research in the area of Learning Classifier Systems
  • Presents a probabilistic approach to Design and Analysis of Learning Classifier Systems

Part of the book series: Studies in Computational Intelligence (SCI)

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Table of contents (10 chapters)

  1. Front Matter

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  2. Introduction

    • Jan Drugowitsch
    Pages 1-11

  3. Background

    • Jan Drugowitsch
    Pages 13-28

  4. A Learning Classifier Systems Model

    • Jan Drugowitsch
    Pages 29-44

  5. A Probabilistic Model for LCS

    • Jan Drugowitsch
    Pages 45-64

  6. Training the Classifiers

    • Jan Drugowitsch
    Pages 65-99

  7. Mixing Independently Trained Classifiers

    • Jan Drugowitsch
    Pages 101-121

  8. The Optimal Set of Classifiers

    • Jan Drugowitsch
    Pages 123-164

  9. An Algorithmic Description

    • Jan Drugowitsch
    Pages 165-201

  10. Towards Reinforcement Learning with LCS

    • Jan Drugowitsch
    Pages 203-235

  11. Concluding Remarks

    • Jan Drugowitsch
    Pages 237-239

  12. Back Matter

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Keywords

About this book

This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition – derived from machine learning – of “a good set of cl- si?ers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of “good set of classi?ers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.

Bibliographic Information

  • Book Title: Design and Analysis of Learning Classifier Systems

  • Book Subtitle: A Probabilistic Approach

  • Authors: Jan Drugowitsch

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-79866-8

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2008

  • Hardcover ISBN: 978-3-540-79865-1Published: 30 May 2008

  • Softcover ISBN: 978-3-642-09861-1Published: 18 November 2010

  • eBook ISBN: 978-3-540-79866-8Published: 17 June 2008

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIV, 267

  • Topics: Artificial Intelligence, Mathematical and Computational Engineering

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