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Generative models for classification use the joint probability distributionof the class variable and the features to construct a decision rule. Amonggenerative models, Bayesian networks and naive Bayes classifiers are the mostcommonly used and provide a clear graphical representation of the relationshipamong all variables. However, these have the disadvantage of highly restrictingthe type of relationships that could exist, by not allowing forcontext-specific independences. Here we introduce a new class of generativeclassifiers, called staged tree classifiers, which formally account forcontext-specific independence. They are constructed by a partitioning of thevertices of an event tree from which conditional independence can be formallyread. The naive staged tree classifier is also defined, which extends theclassic naive Bayes classifier whilst retaining the same complexity. Anextensive simulation study shows that the classification accuracy of stagedtree classifiers is competitive with that of state-of-the-art classifiers andan example showcases their use in practice.

A new class of generative classifiers based on staged tree models