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Bayesian network classifiers based on Gaussian kernel density
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DOI：10.1016/j.eswa.2015.12.031.png)
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Abstract
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For learning a Bayesian network classifier, continuous attributes usually need to be discretized. But the discretization of continuous attributes may bring information missing, noise and less sensitivity to the changing of the attributes towards class variables. In this paper, we use the Gaussian kernel function with smoothing parameter to estimate the density of attributes. Bayesian network classifier with continuous attributes is established by the dependency extension of Naive Bayes classifiers. We also analyze the information provided to a class for each attributes as a basis for the dependency extension of Naive Bayes classifiers. Experimental studies on UCI data sets show that Bayesian network classifiers using Gaussian kernel function provide good classification accuracy comparing to other approaches when dealing with continuous attributes. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Bayesian network classifiers
Continuous attributes
Gaussian kernel function
Smoothing parameters
Classification accuracy
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