Maximum a Posteriori Tree Augmented Naive Bayes Classifiers

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3149

Title:	Maximum a Posteriori Tree Augmented Naive Bayes Classifiers
Keywords:	Artificial Intelligence Bayesian networks Bayesian network classifiers Naive Bayes Decomposable distributions Bayesian model averaging
Publisher:	Springer
Description:	The original publication is available at www.springerlink.com 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 efficiently 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. 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 with equivalent complexity. Peer reviewed
URI:	http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3149
Other Identifiers:	Discovery Science, 7th. International Conference, DS 2004 Padova, Italy, October 2004 Proceedings. Lecture Notes in Artificial Intelligence, Vol. 3245, p.p.: 73-88, Springer Verlag, 2004 3-540-23357-1 0302-9743 http://hdl.handle.net/10261/3149 10.1007/b100845
Appears in Collections:	Digital Csic

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