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Research and Development in Intelligent Systems XXV
Over a decade ago, Friedman et al. introduced the Tree Augmented Naïve Bayes (TAN) classifier, with experiments indicating that it significantly outperformed Naïve Bayes (NB) in terms of classification accuracy, whereas general Bayesian network (GBN) classifiers performed no better than NB. This paper challenges those claims, using a careful experimental analysis to show that GBN classifiers significantly outperform NB on datasets analyzed, and are comparable to TAN performance. It is founddoi:10.1007/978-1-84882-171-2_1 dblp:conf/sgai/Madden08 fatcat:5wog72vw4vaozc3oxaqr3lt464