**Bayesian network classifiers**

**N. Friedman**, D. Geiger, and M. Goldszmidt

*Machine Learning* 29:131--163, 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*. 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 California at Irvine
repository, and compared them to C4.5, naive Bayes, and wrapper-based feature selection
methods.

nir@cs.huji.ac.il