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

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
• Keywords: GECCO 2007, Machine Learning, Michigan-style Learning Fuzzy-Classifier, Supervised Learning Tasks