  Earlier studies have shown that classification accuracies of Bayesian
networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a
class variable, given the feature variables, were higher than those obtained by
maximizing the marginal likelihood (ML). However, differences between the
performances of the two scores in the earlier studies may be attributed to the
fact that they used approximate learning algorithms, not exact ones. This paper
compares the classification accuracies of BNs with approximate learning using
CLL to those with exact learning using ML. The results demonstrate that the
classification accuracies of BNs obtained by maximizing the ML are higher than
those obtained by maximizing the CLL for large data. However, the results also
demonstrate that the classification accuracies of exact learning BNs using the
ML are much worse than those of other methods when the sample size is small and
the class variable has numerous parents. To resolve the problem, we propose an
exact learning augmented naive Bayes classifier (ANB), which ensures a class
variable with no parents. The proposed method is guaranteed to asymptotically
estimate the identical class posterior to that of the exactly learned BN.
Comparison experiments demonstrated the superior performance of the proposed
method.
