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On the Classification Performance of TAN and General Bayesian Networks

ARAN - Access to Research at NUI Galway

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dc.contributor.author Madden, Michael G. en
dc.date.accessioned 2009-06-02T11:48:58Z en
dc.date.available 2009-06-02T11:48:58Z en
dc.date.issued 2008 en
dc.identifier.citation "On the Classification Performance of TAN and General Bayesian Networks", Michael G. Madden. Proceedings of AI-2008, the Twenty-eighth SGAI International Conference, Cambridge, UK, December 2008. en
dc.identifier.uri http://hdl.handle.net/10379/207 en
dc.description.abstract 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. en
dc.format application/pdf en
dc.language.iso en en
dc.subject Bayesian networks and classification en
dc.subject Tree augmented naive Bays classifier (TAN) en
dc.subject Naive Bays (NB) en
dc.subject General Bayesian Network (GBN) en
dc.subject.lcsh Neural networks (Computer science) en
dc.subject.lcsh Bayesian statistical decision theory -- Data processing. en
dc.title On the Classification Performance of TAN and General Bayesian Networks en
dc.type Conference Paper en

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