Tractable Bayesian Learning of Tree Augmented Naive Bayes Classifiers

Abstract

Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions. In this paper we introduce a classifier taking as basis the TAN models and taking into account uncertainty in model selection. To do this we introduce decomposable distributions over TANs and show that the expression resulting from the Bayesian model averaging of TAN models can be integrated into closed form if we assume the prior probability distribution to be a decomposable distribution. This result allows for the construction of a classifier with a shorter learning time and a longer classification time than TAN. Empirical results show that the classifier is, most of the cases, more accurate than TAN and approximates better the class probabilities. 1