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

Abstract

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.