High accuracy Bayesian network classifiers which have asymptotic consistency regardless of whether the data follows a Bayesian network

Abstract

Classification is a central problem in machine learning and requires a classifier. One of the most effective classifiers is a so-called Bayesian network classifier (BNC). Recent studies show that an exact learning of augmented naive Bayes (ANB), which maximizes marginal likelihood (ML) provides higher classification accuracy than any other BNC does. However, maximizing ML has no guarantee to have asymptotic consistency when the true model does not follow a BN. This study proposes a new learning BNC method that asymptotically obtains an I-map with the minimum number of the class variable parameters regardless of whether the true model follows a BN. The proposed method provides more accurate posterior of the class variable than maximizing ML does. Comparison experiments demonstrate the effectiveness of the proposed method.