Augmented BAN Classifier

Learning machine is usually divided to strong learning machines and weak learning machines in machine learning. The result of most individual learning machine is output as while learning machine integration to used for a classification. BAN is an augmented Bayesian network classifier, whose accuracy can be improve by combining several weak learning machines. In this paper, a bagging classifier bagging-BAN-GBN which wraps around GBN and BAN is compared with the boosting-BAN classifier which is boosting based on BAN combination. Finally, experimental results show that the boosting-BAN has higher classification accuracy on most data sets.