Structure learning algorithms for Bayesian network classifiers.
The algorithms are aimed at classification, and favour predictive power over the ability to recover the correct network structure. The implementation in bnlearn assumes that all variables, including the classifiers, are discrete.
Naive Bayes (naive.bayes): a very simple
algorithm assuming that all classifiers are independent and using the
posterior probability of the target variable for classification.
Tree-Augmented Naive Bayes (tree.bayes): an
improvement over naive Bayes, this algorithms uses Chow-Liu to approximate
the dependence structure of the classifiers.
Friedman N, Geiger D, Goldszmit M (1997). "Bayesian Network Classifiers". Machine Learning, 29:131--163.