Evolving Fuzzy Rules with UCS: Preliminary Results

This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS 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 large collection of real-world problems, and compared to UCS and three highly-used machine learning techniques: the decision tree C4.5, the support vector machine SMO, and the fuzzy boosting algorithm Fuzzy LogitBoost. The results show that Fuzzy-UCS is highly competitive with respect to the four learners in terms of performance, and that the fuzzy representation permits a much better understandability of the evolved knowledge. These promising results of the online architecture of Fuzzy-UCS allow for further research and application of the system to new challenging problems.

• Type: Conference
• Year: 2007
• Where: IWCLS
• Authors: Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
• Keywords: Fuzzy Boosting Algorithm, Fuzzy Rules, IWCLS 2007, Machine Learning, Supervised Learning Tasks