Boosting Methodology for Regression Problems
Abstract
Classification problems have dominated research on boosting to date. The application of boosting to regression problems, on the other hand, has received little investigation. In this paper we develop a new boosting method for regression problems. We cast the regression problem as a classification problem and apply an interpretable form of the boosted naïve Bayes classifier. This induces a regression model that we show to be expressible as an additive model for which we derive estimators and discuss computational issues. We compare the performance of our boosted naïve Bayes regression model with other interpretable multivariate regression procedures.
Cite
Text
Ridgeway et al. "Boosting Methodology for Regression Problems." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.Markdown
[Ridgeway et al. "Boosting Methodology for Regression Problems." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.](https://mlanthology.org/aistats/1999/ridgeway1999aistats-boosting/)BibTeX
@inproceedings{ridgeway1999aistats-boosting,
title = {{Boosting Methodology for Regression Problems}},
author = {Ridgeway, Greg and Madigan, David and Richardson, Thomas S.},
booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics},
year = {1999},
volume = {R2},
url = {https://mlanthology.org/aistats/1999/ridgeway1999aistats-boosting/}
}