Building Classifiers using Bayesian Networks
N. Friedman and M. Goldszmidt
National Conf. on Artificial Intelligence (AAAI96).
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 model and explicitly represent statements about independence.
Among these approaches we single out a new method, Tree Augmented
which outperforms naive-Bayes, yet at the same
time maintains the computational simplicity (no search involved) and
which are characteristic of naive-Bayes. We
experimentally tested these approaches using benchmark problems from
the Irvine repository, and compared them against C4.5, naive-Bayes,
and wrapper-based feature selection methods.
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