Learning Classifier Systems : International Workshops, IWLCS 2003-2005, Revised Selected Papers / edited by Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.

Format:

Book

Contributor:

Kovacs, Tim, 1971- editor.

Llorà, Xavier, editor.

Takadama, Keiki, 1970- editor.

Lanzi, Pier Luca, 1967- editor.

Stolzmann, Wolfgang, 1966- editor.

Wilson, Stewart W., 1937- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 4399.

Lecture Notes in Artificial Intelligence ; 4399

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Logic, Symbolic and mathematical.

Data mining.

Artificial Intelligence.

Computation by Abstract Devices.

Mathematical Logic and Formal Languages.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Computation by Abstract Devices.

Mathematical Logic and Formal Languages.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XII, 345 pages).

Edition:

First edition 2007.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-71231-2

9783540712312

Access Restriction:

Restricted for use by site license.