ML Anthology

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

Franz Pernkopf
AAAI 2004 pp. 360-365
doi:10.1016/j.bjoms.2014.01.023 /aaai/2004/pernkopf2004aaai-bayesian/

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.

PDF AAAI Semantic Scholar

Cite

Text

Pernkopf. "Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection." AAAI Conference on Artificial Intelligence, 2004. doi:10.1016/j.bjoms.2014.01.023

Markdown

[Pernkopf. "Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection." AAAI Conference on Artificial Intelligence, 2004.](https://mlanthology.org/aaai/2004/pernkopf2004aaai-bayesian/) doi:10.1016/j.bjoms.2014.01.023

BibTeX

@inproceedings{pernkopf2004aaai-bayesian,
  title     = {{Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection}},
  author    = {Pernkopf, Franz},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2004},
  pages     = {360-365},
  doi       = {10.1016/j.bjoms.2014.01.023},
  url       = {https://mlanthology.org/aaai/2004/pernkopf2004aaai-bayesian/}
}