A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes
Abstract
We propose a method to improve the probability estimates made by Naive Bayes to avoid the effects of poor class conditional probabilities based on product distributions when each class spreads into multiple regions. Our approach is based on applying a clustering algorithm to each subset of examples that belong to the same class, and to consider each cluster as a class of its own. Experiments on 26 real-world datasets show a significant improvement in performance when the class decomposition process is applied, particularly when the mean number of clusters per class is large.
Cite
Text
Vilalta and Rish. "A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes." European Conference on Machine Learning, 2003. doi:10.1007/978-3-540-39857-8_40Markdown
[Vilalta and Rish. "A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes." European Conference on Machine Learning, 2003.](https://mlanthology.org/ecmlpkdd/2003/vilalta2003ecml-decomposition/) doi:10.1007/978-3-540-39857-8_40BibTeX
@inproceedings{vilalta2003ecml-decomposition,
title = {{A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes}},
author = {Vilalta, Ricardo and Rish, Irina},
booktitle = {European Conference on Machine Learning},
year = {2003},
pages = {444-455},
doi = {10.1007/978-3-540-39857-8_40},
url = {https://mlanthology.org/ecmlpkdd/2003/vilalta2003ecml-decomposition/}
}