Probability Estimation for Bayesian Network Classifiers and Decision Trees

This thesis aims to improve the class probability estimation of Bayesian network classifiers and decision trees by hierarchical probability smoothing and ensemble learning techniques. Two new algorithms are proposed in this thesis with the state-of-the-art performance, which is an important contribution for applications that accurate class probability estimates are essential.