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2007
Springer

Evolving Fuzzy Rules with UCS: Preliminary Results

12 years 8 months ago
Evolving Fuzzy Rules with UCS: Preliminary Results
This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS 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.
Albert Orriols-Puig, Jorge Casillas, Ester Bernad&
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where IWCLS
Authors Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
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