Fuzzy-UCS: preliminary results

Authors:
Albert Orriols-Puig;Jorge Casillas;Ester Bernadó-Mansilla
Affiliations:
Enginyeria i Arquitectura La Salle;Granada University;Enginyeria i Arquitectura La Salle
Venue:
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Year:
2007

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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.