|Title||Maximum a Posteriori Tree Augmented Naive Bayes Classifiers|
|Year of Publication||2003|
|Authors||Cerquides J, de Mántaras RLópez|
Bayesian classifiers such as Navie Bayes or Tree Augmented Navie 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 whit 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. Both classifiers have the advantage of allowing the introduction of prior knowledge about structure or parameters into the learning process. Empirical results show that both classifiers lead to an improvement in error rate and accuracy of the predicted class probabilities over established TAN based classifiers whit equivalent complexity.
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