On searching for optimal classifiers among Bayesian networks
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:71-76, 2001.
There is much interest in constructing from datasets Bayesian networks which are efficient, or even optimal, for classification purposes. Most search strategies usually discriminate between networks by comparing their marginal likelihood score, but recently it has been suggested that search strategies for classifiers should instead select among models using alternative scores. This paper contributes to this discussion by presenting the results of simulations on the sets of all directed acyclic graphs on four and five nodes. Our results add evidence to earlier indications that the marginal likelihood is likely to be a poor criterion to use for classifier selection.