Learning Classifier Systems

1. Front Matter

   PDF

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

4. New Directions

   1. A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients

      • Javier G. Marín-Blázquez, Sonia Schulenburg

      Pages 193-218

   2. Adaptive Value Function Approximations in Classifier Systems

      • Lashon B. Booker

      Pages 219-238

   3. Three Architectures for Continuous Action

      • Stewart W. Wilson

      Pages 239-257

   4. A Formal Relationship Between Ant Colony Optimizers and Classifier Systems

      • Lawrence Davis

      Pages 258-269

   5. Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis

      • John H. Holmes

      Pages 270-281

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

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.

• Fuzzy
• adaptive exploration rate
• algorithmic learning
• algorithms
• complexity
• constraints
• data mining
• decision trees
• evolutionary algorithms
• function approximation
• fuzzy sets
• genetic algorithm
• knowledge representation
• learning
• proving

Policies and ethics