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BayesFuzzy: using a Bayesian Classifier to Induce a Fuzzy Rule Base

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4 Author(s)
Estevam R. Hruschka ; Department of Computer Science, Federal University University of Sao Carlos, Rod. Washington Luiz Km 235, Sao Carlos, Brazil. phone: +55 16 33518617; email: ; Heloisa de A. Camargo ; Marcos E. Cintra ; M. do Carmo Nicoletti

Traditional algorithms for learning Bayesian classifiers (BCs) from data are known to induce accurate classification models. However, when using these algorithms, two main concerns should be considered: i) they require qualitative data and ii) generally the induced models are not easily comprehensible by human beings. This paper deals with the two above issues by proposing a hybrid method named BayesFuzzy that learns from quantitative data and induces a fuzzy rule based model that enhances comprehensibility. BayesFuzzy has been implemented as an automatic system that combines a fuzzy strategy, for transforming numerical data into qualitative information, with a Bayes-based approach for inducing rules. Promising empirical results of the use of the BayesFuzzy system in four knowledge domains are presented and discussed.

Published in:

2007 IEEE International Fuzzy Systems Conference

Date of Conference:

23-26 July 2007