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Bayesian Network Classifiers. (1997)
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| Venue: | Machine Learning |
| Citations: | 793 - 20 self |
BibTeX
@INPROCEEDINGS{Friedman97bayesiannetwork,
author = {Nir Friedman and Dan Geiger and Moises Goldszmidt and Gregory Provan},
title = {Bayesian Network Classifiers.},
booktitle = {Machine Learning},
year = {1997},
pages = {131--163}
}
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Abstract
. 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 evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. Bayesian networks are factored representations of probability distributions that generalize the naive Bayesian 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 that are characteristic of naive Bayes. We experimentally tested these approaches, using benchmark problems from the University of Calif...
Keyphrases
naive bayes bayesian network classifier bayesian network restrictive assumption simple bayesian classifier state-of-the-art classifier benchmark problem strong assumption probability distribution represent statement tree augmented naive bayes naive bayesian classifier supervised learning computational simplicity factored representation recent work
