Learning Robust Bayesian Network Classifiers in the Space of Markov Equivalent Classes

Tree Augmented Na¨ýve Bayes(TAN) is a robust classification model. However, so far some researchers still attempt to improve the performance by considering directions of edges, because traditional learning method merely takes into account log likelihood, which is not suitable for learning classifiers, when learning a tree topological structure. In this paper, we analyze search spaces of TAN, research equivalent classes in them. Accordingly, we point out it is not necessary to pay attention to the dependent directions between conditional variables for these directions do not play a role in maximizing log conditional likelihood. For application, we propose a novel framework for learning TAN classifiers. Finally, we run experiments on Weka platform using 45 problems from the University of California at Irvine repository. Experimental results show that classification accuracy and stability do not change statistically in our leraning framework.