Building Classifiers Using Bayesian Networks

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Building Classifiers Using Bayesian Networks
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for inducing classifiers from data, based on recent results in the theory of learning Bayesian networks. Bayesian networks are factored representations of probability distributions that generalize the naive Bayes classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity(no search involved) and robustness which are characteristic of naive Bayes. We experimentally tested these approaches using benchmark problems from...
Nir Friedman, Moisés Goldszmidt
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1996
Where AAAI
Authors Nir Friedman, Moisés Goldszmidt
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