Article

The compact classifier system: motivation, analysis, and first results

Authors:

Xavier Llorà,

Kumara Sastry,

David E. GoldbergAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1993 - 1994

https://doi.org/10.1145/1068009.1068328

Published: 25 June 2005 Publication History

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Abstract

This paper presents an initial analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. In order to be able to perform such analysis we introduce a simple bare-bones Pittsburgh classifier systems---the compact classifier system (CCS)---based on estimation of distribution algorithms. Using a common rule encoding scheme of Pittsburgh classifier systems, CCS maintains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbed probability vectors which represents the rules. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules.

References

[1]

K. A. De Jong and W. M. Spears. Learning Concept Classification Rules using Genetic Algorithms. In Proceedings of the Twelfth International Conference on Artificial Intelligence IJCAI-91, volume 2, pages 651--656. Morgan Kaufmann, 1991.

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[2]

G. Harik, F. Lobo, and D. E. Goldberg. The compact genetic algorithm. Proceedings of the IEEE International Conference on Evolutionary Computation, pages 523--528, 1998. (Also IlliGAL Report No. 97006).

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Google Scholar

[3]

C. Janikow. A Knowledge Intensive Genetic Algorithm for Supervised Learning. Machine Learning, 13:198--228, 1993.

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Google Scholar

[4]

P. Larrañaga and J. A. Lozano, editors. Estimation of Distribution Algorithms. Kluwer Academic Publishers, Boston, MA, 2002.

Crossref

Google Scholar

[5]

X. Llorà and J. Garrell. Knowledge-Independent Data Mining with Fine-Grained Parallel Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2001), pages 461--468. Morgan Kaufmann Publishers, 2001.

Google Scholar

[6]

X. Llorà, K. Sastry, and D. E. Goldberg. Binary Rule Encoding Scheme: A Study Using The Compact Classifier System. In International Workshop on Learning Classifier Systems (IWLCS 2005), accepted, 2005.

Digital Library

Google Scholar

[7]

S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149--175, 1995.

Digital Library

Google Scholar

Cited By

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Urbanowicz RLo CHolmes JMoore JTakadama K(2018)Attribute trackingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205618(553-560)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205618
Llorà XPriya ABhargava R(2009)Observer-invariant histopathology using genetics-based machine learningNatural Computing: an international journal10.1007/s11047-007-9056-68:1(101-120)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1007/s11047-007-9056-6
Llorà XSastry KLima CLobo FGoldberg D(2008)Linkage Learning, Rule Representation, and the Χ-Ary Extended Compact Classifier SystemLearning Classifier Systems10.1007/978-3-540-88138-4_11(189-205)Online publication date: 17-Oct-2008
https://dl.acm.org/doi/10.1007/978-3-540-88138-4_11
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Index Terms

The compact classifier system: motivation, analysis, and first results
1. Computing methodologies
  1. Machine learning

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Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 June 2005

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GECCO05

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Urbanowicz RLo CHolmes JMoore JTakadama K(2018)Attribute trackingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205618(553-560)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205618
Llorà XPriya ABhargava R(2009)Observer-invariant histopathology using genetics-based machine learningNatural Computing: an international journal10.1007/s11047-007-9056-68:1(101-120)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1007/s11047-007-9056-6
Llorà XSastry KLima CLobo FGoldberg D(2008)Linkage Learning, Rule Representation, and the Χ-Ary Extended Compact Classifier SystemLearning Classifier Systems10.1007/978-3-540-88138-4_11(189-205)Online publication date: 17-Oct-2008
https://dl.acm.org/doi/10.1007/978-3-540-88138-4_11
Llorà XSastry KGoldberg D(2007)Binary rule encoding schemesProceedings of the 2003-2005 international conference on Learning classifier systems10.5555/1761381.1761386(40-58)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.5555/1761381.1761386
Llorà XReddy RMatesic BBhargava RLipson H(2007)Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imagingProceedings of the 9th annual conference on Genetic and evolutionary computation10.1145/1276958.1277366(2098-2105)Online publication date: 7-Jul-2007
https://dl.acm.org/doi/10.1145/1276958.1277366
Llorà XSastry KCattolico M(2006)Fast rule matching for learning classifier systems via vector instructionsProceedings of the 8th annual conference on Genetic and evolutionary computation10.1145/1143997.1144244(1513-1520)Online publication date: 8-Jul-2006
https://dl.acm.org/doi/10.1145/1143997.1144244
Butz MPelikan MCattolico M(2006)Studying XCS/BOA learning in Boolean functionsProceedings of the 8th annual conference on Genetic and evolutionary computation10.1145/1143997.1144236(1449-1456)Online publication date: 8-Jul-2006
https://dl.acm.org/doi/10.1145/1143997.1144236
Bacardit JKrasnogor NCattolico M(2006)Smart crossover operator with multiple parents for a Pittsburgh learning classifier systemProceedings of the 8th annual conference on Genetic and evolutionary computation10.1145/1143997.1144235(1441-1448)Online publication date: 8-Jul-2006
https://dl.acm.org/doi/10.1145/1143997.1144235
Llorà XSastry KGoldberg DRothlauf F(2005)Binary rule encoding schemesProceedings of the 7th annual workshop on Genetic and evolutionary computation10.1145/1102256.1102275(88-89)Online publication date: 25-Jun-2005
https://dl.acm.org/doi/10.1145/1102256.1102275

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Binary rule encoding schemes: a study using the compact classifier system

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