Abstract: A method that yields more accurate Bayesian network classifiers when learning from unlabeled data in combination with labeled data includes learning a set of parameters for a structure of a classifier using a set of labeled data and learning a set of parameters for the structure using the labeled data and a set of unlabeled data and then modifying the structure if the parameters based on the labeled and unlabeled data leads to less accuracy in the classifier in comparison to the parameters based on the labeled data only. The present technique enable an increase in the accuracy of a statistically learned Bayesian network classifier when unlabeled data are available and reduces the likelihood of degrading the accuracy of the Bayesian network classifier when using unlabeled data.