Maximum a Posteriori Tree Augmented Naive Bayes Classifiers

Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions. In this paper we prove that under suitable conditions it is possible to calculate efficiently the maximum a posterior TAN model. Furthermore, we prove that it is also possible to calculate a weighted set with the k maximum a posteriori TAN models. This allows efficient TAN ensemble learning and accounting for model uncertainty. These results can be used to construct two classifiers.

• Type: Conference
• Year: 2004
• Where: DIS
• Authors: Jesús Cerquides, Ramon López de Mántaras
• Keywords: DIS 2004, Naive Bayes, Posterior Tan Model, TAN Models, Theoretical Computer Science