Learning Bayesian Network Classifiers with Labeled and Unlabeled Data

Classifiers based on Bayesian networks are usually learned with a fixed structure or a small subset of possible structures. In the presence of unlabeled data this strategy can be detrimental to classification performance, when the assumed classifier structure is incorrect. In this paper we present a classification driven learning method for Bayesian network classifiers that is based on Metropolis-Hastings sampling. We first show that this learning method outperforms existing approaches for fully labeled datasets. We then show that the method is successful in dealing with unlabeled data. Provided we have abundant unlabeled data, the learning method can process scarce labeled data to produce classifiers that attain performance comparable to classifiers learned with large amounts of fully labeled data.