A new class of generative classifiers based on staged tree models

TL;DR

This paper introduces staged tree classifiers, a new class of generative models that explicitly model context-specific independence, offering competitive accuracy and greater flexibility than traditional Bayesian network classifiers.

Contribution

The paper proposes staged tree classifiers, a novel class of generative classifiers that incorporate context-specific independence, extending naive Bayes with similar complexity.

Findings

Staged tree classifiers achieve classification accuracy comparable to state-of-the-art methods.

They effectively model context-specific independences in classification tasks.

The naive staged tree classifier extends naive Bayes while maintaining similar complexity.

Abstract

Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule. Among generative models, Bayesian networks and naive Bayes classifiers are the most commonly used and provide a clear graphical representation of the relationship among all variables. However, these have the disadvantage of highly restricting the type of relationships that could exist, by not allowing for context-specific independences. Here we introduce a new class of generative classifiers, called staged tree classifiers, which formally account for context-specific independence. They are constructed by a partitioning of the vertices of an event tree from which conditional independence can be formally read. The naive staged tree classifier is also defined, which extends the classic naive Bayes classifier whilst retaining the same complexity. An extensive simulation study shows that the classification accuracy of staged tree classifiers is competitive with that of state-of-the-art classifiers and an example showcases their use in practice.