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

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 19

Learning

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.