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245 1 0 _aLearning Classifier Systems
_h[electronic resource] :
_bInternational Workshops, IWLCS 2003-2005, Revised Selected Papers /
_cedited by Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.
250 _a1st ed. 2007.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2007.
300 _aXII, 345 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v4399
505 0 _aKnowledge 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.
520 _aThe 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aComputer science.
_99832
650 0 _aMachine theory.
_9149552
650 0 _aData mining.
_93907
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aTheory of Computation.
_9149553
650 2 4 _aFormal Languages and Automata Theory.
_9149554
650 2 4 _aData Mining and Knowledge Discovery.
_9149555
700 1 _aKovacs, Tim.
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700 1 _aLlorà, Xavier.
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700 1 _aTakadama, Keiki.
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700 1 _aLanzi, Pier Luca.
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700 1 _aStolzmann, Wolfgang.
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700 1 _aWilson, Stewart W.
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830 0 _aLecture Notes in Artificial Intelligence,
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