Bayesian network classifiers versus k-NN classifier using sequential feature selection

Authors:
Franz Pernkopf
Affiliations:
University of Washington, Department of Electrical Engineering, Seattle, WA and Graz University of Technology, Institute of Communications and Wave Propagation, Graz, Austria
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

The aim of this paper is to compare Bayesian network classifiers to the k-NN classifier based on a subset of features. This subset is established by means of sequential feature selection methods. Experimental results show that Bayesian network classifiers more often achieve a better classification rate on different data sets than selective k-NN classifiers. The k-NN classifier performs well in the case where the number of samples for learning the parameters of the Bayesian network is small. Bayesian network classifiers outperform selective k- NN methods in terms of memory requirements and computational demands. This paper demonstrates the strength of Bayesian networks for classification.