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Bayesian Network Classifiers (1997)
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| Citations: | 796 - 20 self |
BibTeX
@MISC{Friedman97bayesiannetwork,
author = {Nir Friedman and Dan Geiger and Moises Goldszmidt},
title = { Bayesian Network Classifiers },
year = {1997}
}
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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. These 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 characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.
Keyphrases
naive bayes bayesian network classifier restrictive assumption bayesian network simple bayesian classifier state-of-the-art classifier strong assumption probability distribution represent statement feature selection computational simplicity tree augmented naive bayes naive bayesian classifier irvine repository supervised learning factored representation recent work
