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Evolving Fuzzy Rules with UCS: Preliminary Results

  • Albert Orriols-Puig
  • Jorge Casillas
  • Ester Bernadó-Mansilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Support Vector Machine Fuzzy Rule Machine Learning Technique Average Rank Linguistic Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Albert Orriols-Puig
    • 1
  • Jorge Casillas
    • 2
  • Ester Bernadó-Mansilla
    • 1
  1. 1.Grup de Recerca en Sistemes Intel·ligents Enginyeria i Arquitectura La SalleUniversitat Ramon LlullBarcelonaSpain
  2. 2.Dept. Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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