research-article

Towards final rule set reduction in XCS: a fuzzy representation approach

Authors:

Farzaneh Shoeleh,

Sattar HashemiAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1211 - 1218

https://doi.org/10.1145/2001576.2001740

Published: 12 July 2011 Publication History

Abstract

Generalization is the most challenging issue in XCS research area. One of the main components of XCS managing to remedy this issue is knowledge representation. In this paper, a knowledge representation based on fuzzy membership function offering certain and vague regions is described. We extend the Michigan learning classifier system using this approach to be improved in terms of both performance and interpretability. The contribution of this paper is three-folds: 1) updating main parameters of classifiers based on their certainty factor in matching of incoming data, 2) enhancing essential components of XCS to be compatible with such fuzzy representation schema and 3) proposing a novel rule set reduction method named Reduction based on Least Reward Prediction (RLRP) to improve the interpretability of the evolved model. Furthermore, an inference methodology which is compatible with RLRP is suggested to maintain the similar performance. The obtained results are promising due to the effectiveness of proposed method in dealing with real world problems. Furthermore, the proposed reduction method can upgrade the interpretability of final rule set by boiling its size down by 94% on average while slightly degrading the prediction accuracy.

References

[1]

J. H. Holland. Adaptation. In R. Rosen and F. Snell, editors, Progress in Theoretical Biology, volume 4, pages 263--293. New York: Academic Press, 1976.

[2]

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

Digital Library

[3]

P.L. Lanzi. Learning classifier systems: then and now. Evolutionary Intelligence. 1(1): 63--82, 2008.

[4]

T. Kovacs. XCS classifier system reliably evolves accurate, complete, and minimal representations for boolean functions. In Roy, Chawdhry, and Pant, editors, Soft Computing in Engineering Design and Manufacturing, pages 59--68, 1997.

[5]

F. Shoeleh, A. Hamzeh and S. Hashemi. To Handle Real Valued Input in XCS: Using Fuzzy Hyper-trapezoidal Membership in Classifier Condition. Simulated Evolution and Learning, pages 55--64, 2010.

Digital Library

[6]

A. Asuncion and D. J. Newman. UCI Machine Learning Repository: {http://www.ics.uci.edu/mlearn /MLRepository.html}. University of California, 2007.

[7]

S. W. Wilson. Get real! XCS with continuous-valued inputs. In Learning Classifier Systems. From Foundations to Applications, LNAI, pages 209--219, Berlin, 2000.

Digital Library

[8]

S. W. Wilson. Mining oblique data with XCS. Advances in Learning Classifier Systems, pages 158--176, 2001.

Digital Library

[9]

C. Stone and L. Bull. For real! XCS with continuous-valued inputs. Evolutionary Computation,11(3):299--336, 2003.

Digital Library

[10]

H. H. Dam, H. A. Abbass, and C. Lokan. Be real! XCS with continuous-valued inputs. In GECCO'05: In Proceedings of the 2005 Genetic and Evolutionary Computation Conference workshop program, pages 85--87, 2005.

Digital Library

[11]

M. V. Butz, P. L. Lanzi, and S. W. Wilson. Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Transactions on Evolutionary Computation, 12(3):355--376, 2008.

Digital Library

[12]

P. L. Lanzi and S. W. Wilson. Using convex hulls to represent classifier conditions. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1481--1488, 2006.

Digital Library

[13]

M. Valenzuela-Rendon. The fuzzy classifier system: A classifier system for continuously varying variables. In 4th ICGA, pages 346--353. Morgan Kaufmann, 1991.

[14]

A. Bonarini and C. Bonacina and M. Matteucci. Fuzzy and crisp representations of real-valued input for learning classifier systems. Learning Classifier Systems, pages 107--124, 2000.

Digital Library

[15]

A. Bonarini. An introduction to learning fuzzy classifier systems. Learning Classifier Systems, pages 83--104, 2000.

Digital Library

[16]

J. Casillas, B. Carse, and L. Bull. Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems, 15(4):536--550, 2007.

Digital Library

[17]

A. Orriols-Puig, J. Casillas, and E. Bernado-Mansilla. Fuzzy-UCS: Preliminary results. In GECCO'07: Proceedings of the 2007 Genetic and Evolutionary Computation Conference Workshop Program, volume 3, pages 2871--2874, 2007.

Digital Library

[18]

A. Orriols-Puig, J. Casillas, and E. Bernado-Mansilla. Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning. IEEE Transactions on Evolutionary Computation, 13(2): 260--283, 2009.

Digital Library

[19]

S. W. Wilson. Classifier conditions using gene expression programming. Technical report, IlliGAL Report No. 2008001, Urbana-Champaign IL 61801, USA, 2008.

[20]

L. Bull and T. O'Hara. Accuracy-based neuro and neuro-fuzzy classifier systems. In GECCO'02: Proceedings of the 2002 Genetic and Evolutionary Computation Conference, pages 905--911, 2002.

Digital Library

[21]

E. Bernado-Mansilla and J. M. Garrell. Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Evolutionary Computation, 11(3):209--238, 2003.

Digital Library

[22]

M. V. Butz. Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design, volume 109 of Studies in Fuzziness and Soft Computing. Springer, 2006.

[23]

T. G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7):1895--1924, 1998.

Digital Library

[24]

F. Wilcoxon. Individual comparisons by ranking methods. Biometrics, 1:80--83, 1945.

Cited By

Shiraishi HYe RIshibuchi HNakata M(2024)A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier SystemsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_24(386-402)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70071-2_24
Shiraishi HHayamizu YHashiyama TSilva SPaquete L(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
https://dl.acm.org/doi/10.1145/3583131.3590360
Chen GDouch CZhang M(2015)Using Learning Classifier Systems to Learn Stochastic Decision PoliciesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.241546419:6(885-902)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TEVC.2015.2415464
Show More Cited By

Index Terms

Towards final rule set reduction in XCS: a fuzzy representation approach
1. Computing methodologies
  1. Machine learning
    1. Learning settings

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

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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Published: 12 July 2011

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GECCO '11

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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

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Cited By

Shiraishi HYe RIshibuchi HNakata M(2024)A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier SystemsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_24(386-402)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70071-2_24
Shiraishi HHayamizu YHashiyama TSilva SPaquete L(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
https://dl.acm.org/doi/10.1145/3583131.3590360
Chen GDouch CZhang M(2015)Using Learning Classifier Systems to Learn Stochastic Decision PoliciesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.241546419:6(885-902)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TEVC.2015.2415464
Shiraishi HIshibuchi HNakata M(undefined)A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier SystemsACM Transactions on Evolutionary Learning and Optimization10.1145/3717613
https://dl.acm.org/doi/10.1145/3717613

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