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Título : Maximum a Posteriori Tree Augmented Naive Bayes Classifiers
Autor : Cerquides, Jesus; Lopez de Mantaras, Ramon
Palabras clave : Artificial Intelligence
Bayesian networks
Bayesian network classifiers
Naive Bayes
Decomposable distributions
Bayesian model averaging
Fecha de publicación : 2004
Editor: Springer
Citación : 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
Resumen: 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.
Descripción : The original publication is available at www.springerlink.com
Versión del editor: http://dx.doi.org/10.1007/b100845
URI : http://hdl.handle.net/10261/3149
DOI: 10.1007/b100845
ISBN : 3-540-23357-1
ISSN: 0302-9743
Aparece en las colecciones: (IIIA) Comunicaciones congresos
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