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Be real! XCS with continuous-valued inputs

Published: 25 June 2005 Publication History

Abstract

XCS is widely accepted as one of the most reliable Michigan-style learning classifier system (LCS) for data mining. In order to handle real-valued inputs effectively, the traditional ternary representation has been replaced by the interval-based representation and the modified XCS has shown to work well. Existing interval-based representations still suffer from a few drawbacks which this paper address. In this paper, we propose an alternative approach called the Min-Percentage representation which produces comparable results to other methods in the literature with the extra advantage of overcoming the drawbacks in these methods.

References

[1]
M. V. Butz, D. E. Goldberg, and K. Tharakunnel. Analysis and improvement of fitness exploitation in XCS: Bounding models, tournament selection, and bilateral accuracy. Evolutionary Computation, 11(3):239--277, 2003.
[2]
D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addision-Wesley Publishing Company, INC., 1989.
[3]
J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. Republished by the MIT press, 1992.
[4]
C. Stone and L. Bull. For real! XCS with continuous-valued inputs. Evolutionary Computation, 11(3):299--336, 2003.
[5]
S. W. Wilson. Get real! XCS with continuous-valued inputs. In P. Lanzi, W. Stolzmann, and S. Wilson, editors, Learning Classifier Systems, From Foundations to Applications, LNAI-1813, pages 209--219, Berlin, 2000.
[6]
S. W. Wilson. Mining oblique data with XCS. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Proceedings of the Third International Workshop (IWLCS-2000), Lecture Notes in Artificial Intelligence, pages 158--174, 2001.

Cited By

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  • (2024)XCS: Is Covering All You Need?Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664146(1788-1796)Online publication date: 14-Jul-2024
  • (2023)Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning AlgorithmsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596416(1657-1662)Online publication date: 15-Jul-2023
  • (2023)Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier SystemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590360(548-557)Online publication date: 15-Jul-2023
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cover image ACM Conferences
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation
June 2005
431 pages
ISBN:9781450378000
DOI:10.1145/1102256
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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Publication History

Published: 25 June 2005

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Author Tags

  1. XCS
  2. interval representation
  3. learning classifier system
  4. ternary representation

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Cited By

View all
  • (2024)XCS: Is Covering All You Need?Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664146(1788-1796)Online publication date: 14-Jul-2024
  • (2023)Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning AlgorithmsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596416(1657-1662)Online publication date: 15-Jul-2023
  • (2023)Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier SystemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590360(548-557)Online publication date: 15-Jul-2023
  • (2022)XCSF under limited supervisionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534046(2080-2085)Online publication date: 9-Jul-2022
  • (2022)Can the same rule representation change its matching area?Proceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528874(431-439)Online publication date: 8-Jul-2022
  • (2022)Internalizing Knowledge for Anticipatory Classifier Systems in Discretized Real-Valued EnvironmentsIEEE Access10.1109/ACCESS.2022.316292510(33816-33828)Online publication date: 2022
  • (2020)Optimality-Based Analysis of XCSF Compaction in Discrete Reinforcement LearningParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58115-2_33(471-484)Online publication date: 2-Sep-2020
  • (2019)Preliminary tests of a real-valued anticipatory classifier systemProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326797(1289-1294)Online publication date: 13-Jul-2019
  • (2017)A Survey of Learning Classifier Systems in Games [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2016.262767012:1(42-55)Online publication date: 1-Feb-2017
  • (2016)Fuzzy strength-based XCS: An application on multi-step environment problems2016 Management and Innovation Technology International Conference (MITicon)10.1109/MITICON.2016.8025233(MIT-122-MIT-127)Online publication date: Oct-2016
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