Naive Bayesian Classifiers with Extreme Probability Features
Abstract
Despite their popularity, naive Bayesian classifiers are not well suited for real-world applications involving extreme probability features. As will be demonstrated in this paper, methods used to forestall the inclusion of zero probability parameters in naive classifiers have quite counterintuitive effects. An elegant, principled solution for handling extreme probability events is available however, from coherent conditional probability theory. We will show how this theory can be integrated in standard naive Bayesian classifiers, and then present a computational framework that retains the classifiers’ efficiency in the presence of a limited number of extreme probability features.
Cite
Text
van der Gaag and Capotorti. "Naive Bayesian Classifiers with Extreme Probability Features." Proceedings of the Ninth International Conference on Probabilistic Graphical Models, 2018.Markdown
[van der Gaag and Capotorti. "Naive Bayesian Classifiers with Extreme Probability Features." Proceedings of the Ninth International Conference on Probabilistic Graphical Models, 2018.](https://mlanthology.org/pgm/2018/vandergaag2018pgm-naive/)BibTeX
@inproceedings{vandergaag2018pgm-naive,
title = {{Naive Bayesian Classifiers with Extreme Probability Features}},
author = {van der Gaag, Linda C. and Capotorti, Andrea},
booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models},
year = {2018},
pages = {499-510},
volume = {72},
url = {https://mlanthology.org/pgm/2018/vandergaag2018pgm-naive/}
}