Automatic Construction of Fuzzy Rule Bases: a further Investigation into two Alternative Inductive Approaches
Marcos Evandro Cintra (Federal University of São Carlos, Brazil)
Heloisa Arruda Camargo (Federal University of São Carlos, Brazil)
Estevam R. Hruschka (Federal University of São Carlos, Brazil)
Maria do Carmo Nicoletti (Federal University of São Carlos, Brazil)
Abstract: The definition of the Fuzzy Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. This paper discusses the results of two different hybrid methods, previously investigated, for the automatic generation of fuzzy rules from numerical data. One of the methods, named DoC-based, proposes the creation of Fuzzy Rule Bases using genetic algorithms in association with a heuristic for preselecting candidate rules based on the degree of coverage. The other, named BayesFuzzy, induces a Bayesian Classifier using a dataset previously granulated by fuzzy partitions and then translates it into a Fuzzy Rule Base. A comparative analysis between both approaches focusing on their main characteristics, strengths/weaknesses and easiness of use is carried out. The reliability of both methods is also compared by analyzing their results in a few knowledge domains.
Keywords: Bayesian classification, Bayesian networks, fuzzy logics, genetic fuzzy systems, machine learning
Categories: I.2, I.2.6