  Supervised classification is one of the most ubiquitous tasks in machine
learning. Generative classifiers based on Bayesian networks are often used
because of their interpretability and competitive accuracy. The widely used
naive and TAN classifiers are specific instances of Bayesian network
classifiers with a constrained underlying graph. This paper introduces novel
classes of generative classifiers extending TAN and other famous types of
Bayesian network classifiers. Our approach is based on staged tree models,
which extend Bayesian networks by allowing for complex, context-specific
patterns of dependence. We formally study the relationship between our novel
classes of classifiers and Bayesian networks. We introduce and implement
data-driven learning routines for our models and investigate their accuracy in
an extensive computational study. The study demonstrates that models embedding
asymmetric information can enhance classification accuracy.
