BayesFuzzy: using a Bayesian Classifier to Induce a Fuzzy Rule Base
Estevam Rafael Hruschka Junior, Heloisa A. Camargo, Marcos E. Cintra, Maria do Carmo Nicoletti · Proceedings of ... IEEE International Conference on Fuzzy Systems · 2007
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.