Bayesian network classifiers versus selective k-NN classifier

Authors:
Franz Pernkopf
Affiliations:
Institute of Signal Processing and Speech Communication, Graz University of Technology, Inffeldgasse 12, A-8010 Graz, Austria and Department of Electrical Engineering, University of Washington, M2 ...
Venue:
Pattern Recognition
Year:
2005

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Abstract

In this paper Bayesian network classifiers are compared to the k-nearest neighbor (k-NN) classifier, which is based on a subset of features. This subset is established by means of sequential feature selection methods. Experimental results on classifying data of a surface inspection task and data sets from the UCI repository show that Bayesian network classifiers are competitive with selective k-NN classifiers concerning classification accuracy. 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.