Approximate Versus Linguistic Representation in Fuzzy-UCS

Abstract. This paper introduces an approximate fuzzy representation to FuzzyUCS, 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.

• Type: Conference
• Year: 2008
• Where: HAIS
• Authors: Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
• Keywords: Approximate Fuzzy Representation, Artificial Intelligence, HAIS 2008, Learning Fuzzy-Classifier System, Linguistic Fuzzy Rules