TAN Classifiers Based on Decomposable Distributions

Authors:
Jesús Cerquides;Ramon López Mántaras
Affiliations:
Dept. de Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain 08007;Institut d'Investigació en Intel.ligència Artificial, CSIC, Campus UAB, Bellaterra, Spain 08190
Venue:
Machine Learning
Year:
2005

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Abstract

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. We show that these classifiers provide consistently better predictions over Irvine datasets and artificially generated data than TAN based classifiers proposed in the literature.