Approximate Versus Linguistic Representation in Fuzzy-UCS

Authors:
Albert Orriols-Puig;Jorge Casillas;Ester Bernadó-Mansilla
Affiliations:
Grup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Barcelona, (Spain) 08022;Dept. Computer Science and Artificial Intelligence, University of Granada, Granada, (Spain) 18071;Grup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Barcelona, (Spain) 08022
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

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Abstract

This paper introduces an approximate fuzzy representation to Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies whether the flexibility provided by the approximate representation results in a significant improvement of the accuracy of the models evolved by the system. We test Fuzzy-UCS with both approximate and linguistic representation on a large collection of real-life problems and compare the results in terms of training and test accuracy and interpretability of the evolved rule sets.