Searching for Dependencies in Bayesian Classifiers
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
Text
Pazzani. "Searching for Dependencies in Bayesian Classifiers." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Pazzani. "Searching for Dependencies in Bayesian Classifiers." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/pazzani1995aistats-searching/)BibTeX
@inproceedings{pazzani1995aistats-searching,
title = {{Searching for Dependencies in Bayesian Classifiers}},
author = {Pazzani, Michael J.},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {424-429},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/pazzani1995aistats-searching/}
}