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On the Classification Performance of TAN and General Bayesian Networks
Madden, Michael G.
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 found that the poor performance reported by Friedman et al. are not attributable to the GBN per se, but rather to their use of simple empirical frequencies to estimate GBN parameters, whereas basic parameter smoothing (used in their TAN analyses but not their GBN analyses) improves GBN performance significantly. It is concluded that, while GBN classifiers may have some limitations, they deserve greater attention, particularly in domains where insight into classification decisions, as well as good accuracy, is required.
Keyword(s): Bayesian networks and classification; Tree augmented naive Bays classifier (TAN); Naive Bays (NB); General Bayesian Network (GBN); Neural networks (Computer science); Bayesian statistical decision theory -- Data processing.
Publication Date:
Type: Conference item
Peer-Reviewed: Unknown
Language(s): English
Institution: NUI Galway
File Format(s): application/pdf
First Indexed: 2015-11-10 09:21:03 Last Updated: 2019-09-20 06:26:49