Abstract
The Bayesian network can be considered as a probabilistic classifier with the ability of giving a clear insight into the structural relationships in the domain under investigation. In this paper we use some methodologies of feature subset selection in order to determine the relevant variables which are then used for constructing the Bayesian network. To test how the selected methods of feature selection affect the classification, we consider several Bayesian classifiers: Naïve Bayes, Tree Augmented Naïve Bayes and the general Bayesian network, which is used as benchmark for the comparison.
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Brogini, A., Slanzi, D. (2010). Several Computational Studies About Variable Selection for Probabilistic Bayesian Classifiers. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_23
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DOI: https://doi.org/10.1007/978-3-642-03739-9_23
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