TAN Classifiers Based on Decomposable Distributions

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3019

Title:	TAN Classifiers Based on Decomposable Distributions
Keywords:	Artificial Intelligence Bayesian networks classifiers Naive Bayes Tree augmented naive Bayes Decomposable distributions Bayesian model averaging
Publisher:	Springer
Description:	The original publication is available at www.springerlink.com In this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN's, we can compute the exact Bayesian model averaging over TAN structures and parameters in polynomial time. Furthermore, we prove that the k-maximum a posteriori (MAP) TAN structures can also be computed in polynomial time. We use these results to correct minor errors in Meila & Jaakkola (2000a) and to construct several TAN based classifiers provide consistently better predictions over Irvine datasets and artificially generated data than TAN based classifiers proposed in the literature. Peer reviewed
URI:	http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3019
Other Identifiers:	Machine Learning, 2005, 59 (3): 323-354 0885-6125 http://hdl.handle.net/10261/3019
Appears in Collections:	Digital Csic

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