Boosting-based TAN combination classifier

Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it does not make much more improvement of a stable learning algorithm. TAN, tree-augmented naive Bayes, is a tree-like Bayesian network. The standard TAN learning algorithm generates a stable TAN classifier, whose accuracy is difficult to improve by the Boosting technique. In this paper, a new TAN learning algorithm called GTAN is presented, and multiple TAN classifiers generated by GTAN are combined by Boosting-MultiTAN. Finally, this TAN combination classifier is compared with the standard TAN classifier by the experiments. Experimental results show that the Boosting-MultiTAN has higher classification accuracy than the standard TAN classifier on most data sets.