Article

Fuzzy-UCS: preliminary results

Authors:

Albert Orriols-Puig,

Jorge Casillas,

Ester Bernadó-MansillaAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

Pages 2871 - 2874

https://doi.org/10.1145/1274000.1274059

Published: 07 July 2007 Publication History

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Abstract

This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. Fuzzy-UCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a set of real-world problems, and compared to UCS and two of the most used machine learning techniques: C4.5 and SMO. The results show that Fuzzy-UCS is highly competitive to the three learners in terms of performance, and that the fuzzy representation permits a much better understandability of the evolved knowledge. These promising results allow for further investigation on Fuzzy-UCS.

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Cited By

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Jahani Moghaddam M(2024)A Survey on Genetic Fuzzy SystemsArchives of Computational Methods in Engineering10.1007/s11831-024-10157-9Online publication date: 27-Jun-2024
https://doi.org/10.1007/s11831-024-10157-9
Shiraishi HHayamizu YSato HTakadama KWagner M(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
https://dl.acm.org/doi/10.1145/3512290.3528874
Nievola JRomao L(2012)Predicting GPCR and enzymes function with a global approach based on LCSProceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)10.1109/BIBE.2012.6399665(151-156)Online publication date: 11-Nov-2012
https://dl.acm.org/doi/10.1109/BIBE.2012.6399665
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Index Terms

Fuzzy-UCS: preliminary results
1. Computing methodologies
  1. Machine learning

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Information

Published In

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

July 2007

1450 pages

ISBN:9781595936981

DOI:10.1145/1274000

Program Chairs:
Peter A. N. Bosman
Centre for Mathematics & Computer Science, The Netherlands
,
Tina Yu
Memorial University of Newfoundland, Canada
,
Anikó Ekárt
Aston University, UK

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

New York, NY, United States

Publication History

Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, United Kingdom

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

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Cited By

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Jahani Moghaddam M(2024)A Survey on Genetic Fuzzy SystemsArchives of Computational Methods in Engineering10.1007/s11831-024-10157-9Online publication date: 27-Jun-2024
https://doi.org/10.1007/s11831-024-10157-9
Shiraishi HHayamizu YSato HTakadama KWagner M(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
https://dl.acm.org/doi/10.1145/3512290.3528874
Nievola JRomao L(2012)Predicting GPCR and enzymes function with a global approach based on LCSProceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)10.1109/BIBE.2012.6399665(151-156)Online publication date: 11-Nov-2012
https://dl.acm.org/doi/10.1109/BIBE.2012.6399665
Romão LNievola J(2012)Hierarchical Classification of Gene Ontology with Learning Classifier SystemsAdvances in Artificial Intelligence – IBERAMIA 201210.1007/978-3-642-34654-5_13(120-129)Online publication date: 2012
https://doi.org/10.1007/978-3-642-34654-5_13
Kovacs T(2012)Genetics-Based Machine LearningHandbook of Natural Computing10.1007/978-3-540-92910-9_30(937-986)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_30
Shoeleh FHamzeh AHashemi SLanzi P(2011)Towards final rule set reduction in XCSProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001740(1211-1218)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001576.2001740
Shoeleh FHamzeh AHashemi S(2010)To handle real valued input in XCSProceedings of the 8th international conference on Simulated evolution and learning10.5555/1947457.1947464(55-64)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.5555/1947457.1947464
Toledo-Suárez CPelikan MBranke J(2010)A first assessment of the use of extended relational alphabets in accuracy classifier systemsProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830666(991-998)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830666
Shoeleh FHamzeh AHashemi S(2010)To Handle Real Valued Input in XCS: Using Fuzzy Hyper-trapezoidal Membership in Classifier ConditionSimulated Evolution and Learning10.1007/978-3-642-17298-4_5(55-64)Online publication date: 2010
https://doi.org/10.1007/978-3-642-17298-4_5
Urbanowicz RMoore J(2009)Learning classifier systemsJournal of Artificial Evolution and Applications10.5555/1644490.16444912009(1-25)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.5555/1644490.1644491
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Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning

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