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2004

Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection

9 years 19 days ago
Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection
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 kNN methods in terms of memory requirements and computational demands. This paper demonstrates the strength of Bayesian networks for classification.
Franz Pernkopf
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where AAAI
Authors Franz Pernkopf
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