JUCS - Journal of Universal Computer Science 14(15): 2456-2470, doi: 10.3217/jucs-014-15-2456
Automatic Construction of Fuzzy Rule Bases: a further Investigation into two Alternative Inductive Approaches
expand article infoMarcos Evandro Cintra‡, Heloisa Arruda Camargo‡, Estevam R. Hruschka‡, Maria do Carmo Nicoletti‡
‡ Federal University of São Carlos, São Carlos, Brazil

Corresponding author: Marcos Cintra ( marcos_cintra@dc.ufscar.br )

© Marcos Cintra, Heloisa Camargo, Estevam Hruschka, Maria do Carmo Nicoletti.
This article is freely available under the J.UCS Open Content License.
Citation: Cintra ME, Camargo HA, Hruschka ER, Nicoletti MC (2008) Automatic Construction of Fuzzy Rule Bases: a further Investigation into two Alternative Inductive Approaches. JUCS - Journal of Universal Computer Science 14(15): 2456-2470. https://doi.org/10.3217/jucs-014-15-2456
Open Access
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
machine learning, fuzzy logics, Bayesian networks, Bayesian classification, genetic fuzzy systems