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

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 comprehensib...

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Bibliographic Details
Main Authors: Hruschka, E.R., de Camargo, H., Cintra, M.E., do Nicoletti, M.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Fuzzy set theory
Fuzzy systems
Heuristic algorithms
Humans
Learning systems
Probability distribution
Proposals
Random variables
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Summary: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.
ISSN:1098-7584
DOI:10.1109/FUZZY.2007.4295637