Learning Classifier Systems

This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.

Inhalt

Knowledge Representation.- Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS.- Use of Learning Classifier System for Inferring Natural Language Grammar.- Backpropagation in Accuracy-Based Neural Learning Classifier Systems.- Binary Rule Encoding Schemes: A Study Using the Compact Classifier System.- Mechanisms.- Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System.- Post-processing Clustering to Decrease Variability in XCS Induced Rulesets.- LCSE: Learning Classifier System Ensemble for Incremental Medical Instances.- Effect of Pure Error-Based Fitness in XCS.- A Fuzzy System to Control Exploration Rate in XCS.- Counter Example for Q-Bucket-Brigade Under Prediction Problem.- An Experimental Comparison Between ATNoSFERES and ACS.- The Class Imbalance Problem in UCS Classifier System: A Preliminary Study.- Three Methods for Covering Missing Input Data in XCS.- New Directions.- A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients.- Adaptive Value Function Approximations in Classifier Systems.- Three Architectures for Continuous Action.- A Formal Relationship Between Ant Colony Optimizers and Classifier Systems.- Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis.- Application-Oriented Research and Tools.- Data Mining in Learning Classifier Systems: Comparing XCS with GAssist.- Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule.- Using XCS to Describe Continuous-Valued Problem Spaces.- The EpiXCS Workbench: A Tool for Experimentation and Visualization.