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Evolving Fuzzy Rules with UCS: Preliminary Results

  • Conference paper
Learning Classifier Systems (IWLCS 2006, IWLCS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4998))

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 large collection of real-world problems, and compared to UCS and three highly-used machine learning techniques: the decision tree C4.5, the support vector machine SMO, and the fuzzy boosting algorithm Fuzzy LogitBoost. The results show that Fuzzy-UCS is highly competitive with respect to the four learners in terms of performance, and that the fuzzy representation permits a much better understandability of the evolved knowledge. These promising results of the online architecture of Fuzzy-UCS allow for further research and application of the system to new challenging problems.

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Author information

Authors and Affiliations

  1. Grup de Recerca en Sistemes Intel·ligents Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022, Barcelona, Spain

    Albert Orriols-Puig & Ester Bernadó-Mansilla

  2. Dept. Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Jorge Casillas

Authors
  1. Albert Orriols-Puig
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  2. Jorge Casillas
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  3. Ester Bernadó-Mansilla
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Editor information

Editors and Affiliations

  1. University of Nottingham, School of Computer Science, ASAP research group, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, Sutton Bonington, LE12 5RD, UK

    Jaume Bacardit

  2. Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2, Universitat Ramon Llull, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  3. Department of Psychology, University of Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

  4. Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Tim Kovacs

  5. Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL), University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, 61801-2996, Urbana, IL, USA

    Xavier Llorà

  6. Tokyo Institute of Technology, 152-8550, tokyo, Japan

    Keiki Takadama

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Orriols-Puig, A., Casillas, J., Bernadó-Mansilla, E. (2008). Evolving Fuzzy Rules with UCS: Preliminary Results. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_4

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  • DOI: https://doi.org/10.1007/978-3-540-88138-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88137-7

  • Online ISBN: 978-3-540-88138-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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