  In this paper we present a new Bayesian network model for classification that
combines the naive-Bayes (NB) classifier and the finite-mixture (FM)
classifier. The resulting classifier aims at relaxing the strong assumptions on
which the two component models are based, in an attempt to improve on their
classification performance, both in terms of accuracy and in terms of
calibration of the estimated probabilities. The proposed classifier is obtained
by superimposing a finite mixture model on the set of feature variables of a
naive Bayes model. We present experimental results that compare the predictive
performance on real datasets of the new classifier with the predictive
performance of the NB classifier and the FM classifier.
