Searching for Dependencies in Bayesian Classifiers

Michael J. Pazzani

Searching for Dependencies in Bayesian Classifiers

Michael J. Pazzani

Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:424-429, 1995.

Abstract

In this paper, we explore an alternate approach to determining whether it is useful to join two attributes when constructing a Bayesian classifier. We also give experimental results on parity functions, an artificial set of functions that are particularly difficult for naive Bayesian classifiers, and results on three naturally occurring data sets.

Cite this Paper

BibTeX


@InProceedings{pmlr-vR0-pazzani95a,
  title = 	 {Searching for Dependencies in Bayesian Classifiers},
  author =       {Pazzani, Michael J.},
  booktitle = 	 {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {424--429},
  year = 	 {1995},
  editor = 	 {Fisher, Doug and Lenz, Hans-Joachim},
  volume = 	 {R0},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {04--07 Jan},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/r0/pazzani95a/pazzani95a.pdf},
  url = 	 {https://proceedings.mlr.press/r0/pazzani95a.html},
  abstract = 	 {In this paper, we explore an alternate approach to determining whether it is useful to join two attributes when constructing a Bayesian classifier. We also give experimental results on parity functions, an artificial set of functions that are particularly difficult for naive Bayesian classifiers, and results on three naturally occurring data sets.},
  note =         {Reissued by PMLR on 01 May 2022.}
}

Endnote

%0 Conference Paper
%T Searching for Dependencies in Bayesian Classifiers
%A Michael J. Pazzani
%B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics
%C Proceedings of Machine Learning Research
%D 1995
%E Doug Fisher
%E Hans-Joachim Lenz	
%F pmlr-vR0-pazzani95a
%I PMLR
%P 424--429
%U https://proceedings.mlr.press/r0/pazzani95a.html
%V R0
%X In this paper, we explore an alternate approach to determining whether it is useful to join two attributes when constructing a Bayesian classifier. We also give experimental results on parity functions, an artificial set of functions that are particularly difficult for naive Bayesian classifiers, and results on three naturally occurring data sets.
%Z Reissued by PMLR on 01 May 2022.

APA


Pazzani, M.J.. (1995). Searching for Dependencies in Bayesian Classifiers. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:424-429 Available from https://proceedings.mlr.press/r0/pazzani95a.html. Reissued by PMLR on 01 May 2022.

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