Fuzzy-UCS: preliminary results

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Fuzzy-UCS: preliminary results
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 rulesets. 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. Categories and Subject Descriptors I.2.6 [Learning]: concept learning, knowledge acquisition General Terms Algorithms Keywords Evolutionary Computation, Genetic Algorithms, Machine Learning, Learning Classifier Systems, Fuzzy Logic
Albert Orriols-Puig, Jorge Casillas, Ester Bernad&
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Authors Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
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