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Bayesian network classifiers for probability-based metrics

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A large number of distance metrics have been proposed to measure the difference of two instances. Among these metrics, Short and Fukunaga metric (SFM) and minimum risk metric (MRM) are two probability-based metrics which are widely used to find reasonable distance between each pair of instances with nominal attributes only. For simplicity, existing works use naive Bayesian (NB) classifiers to estimate class membership probabilities in SFM and MRM. However, it has been proved that the ability of NB classifiers to class probability estimation is poor. In order to scale up the classification performance of NB classifiers, many augmented NB classifiers are proposed. In this paper, we study the class probability estimation performance of these augmented NB classifiers and then use them to estimate the class membership probabilities in SFM and MRM. The experimental results based on a large number of University of California, Irvine (UCI) data-sets show that using these augmented NB classifiers to estimate the class membership probabilities in SFM and MRM can significantly enhance their generalisation ability.
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Keywords: Bayesian network classifiers; class probability estimation; classification; data mining; probability-based metrics

Document Type: Research Article

Affiliations: Department of Mathematics, China University of Geosciences, Wuhan,Hubei,430074, P.R. China

Publication date: December 1, 2013

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