Bayesian Network Classifiers

Nir Friedman*, Dan Geiger, Moises Goldszmidt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3995 Scopus citations


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.

Original languageAmerican English
Pages (from-to)131-163
Number of pages33
JournalMachine Learning
Issue number2-3
StatePublished - 1997
Externally publishedYes


  • Bayesian networks
  • Classification


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