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Learning Classifier Systems [electronic resource] :International Workshops, IWLCS 2003-2005, Revised Selected Papers /

Contributor(s): Kovacs, Tim [editor.] | Llorà, Xavier [editor.] | Takadama, Keiki [editor.] | Lanzi, Pier Luca [editor.] | Stolzmann, Wolfgang [editor.] | Wilson, Stewart W [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 4399Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.Description: XII, 345 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540712312.Subject(s): Computer science | Computers | Mathematical logic | Data mining | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Computation by Abstract Devices | Mathematical Logic and Formal Languages | Data Mining and Knowledge DiscoveryOnline resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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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