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Learning Classifier Systems

International Workshops, IWLCS 2003-2005, Revised Selected Papers

  • Conference proceedings
  • © 2007

Overview

Editors:
  1. Tim Kovacs
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  2. Xavier Llorà
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  3. Keiki Takadama
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  4. Pier Luca Lanzi
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  5. Wolfgang Stolzmann
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  6. Stewart W. Wilson
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4399)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: IWLCS 2003. IWLCS 2004. IWLCS 2005.

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Table of contents (22 papers)

  1. Front Matter

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  2. Knowledge Representation

    1. Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS

      • Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai
      Pages 1-16

    2. Use of Learning Classifier System for Inferring Natural Language Grammar

      • Olgierd Unold, Grzegorz Dabrowski
      Pages 17-24

    3. Backpropagation in Accuracy-Based Neural Learning Classifier Systems

      • Toby O’Hara, Larry Bull
      Pages 25-39

    4. Binary Rule Encoding Schemes: A Study Using the Compact Classifier System

      • Xavier Llorà, Kumara Sastry, David E. Goldberg
      Pages 40-58

  3. Mechanisms

    1. Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System

      • Jaume Bacardit, Josep Maria Garrell
      Pages 59-79

    2. Post-processing Clustering to Decrease Variability in XCS Induced Rulesets

      • Flavio Baronti, Alessandro Passaro, Antonina Starita
      Pages 80-92

    3. LCSE: Learning Classifier System Ensemble for Incremental Medical Instances

      • Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, Da-qian Gu
      Pages 93-103

    4. Effect of Pure Error-Based Fitness in XCS

      • Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
      Pages 104-114

    5. A Fuzzy System to Control Exploration Rate in XCS

      • Ali Hamzeh, Adel Rahmani
      Pages 115-127

    6. Counter Example for Q-Bucket-Brigade Under Prediction Problem

      • Atsushi Wada, Keiki Takadama, Katsunori Shimohara
      Pages 128-143

    7. An Experimental Comparison Between ATNoSFERES and ACS

      • Samuel Landau, Olivier Sigaud, Sébastien Picault, Pierre Gérard
      Pages 144-160

    8. The Class Imbalance Problem in UCS Classifier System: A Preliminary Study

      • Albert Orriols-Puig, Ester Bernadó-Mansilla
      Pages 161-180

    9. Three Methods for Covering Missing Input Data in XCS

      • John H. Holmes, Jennifer A. Sager, Warren B. Bilker
      Pages 181-192

  5. Application-Oriented Research and Tools

    1. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist

      • Jaume Bacardit, Martin V. Butz
      Pages 282-290
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  1. Learning Classifier Systems

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About this book

The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi?er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di?erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e?ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di?erent knowledge representations and the LCS methods used to manipulate them.

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