Rule-Enhanced Penalized Regression by Column Generation Using Rectangular Maximum Agreement
Abstract
We describe a learning procedure enhancing L1-penalized regression by adding dynamically generated rules describing multidimensional “box” sets. Our rule-adding procedure is based on the classical column generation method for high-dimensional linear programming. The pricing problem for our column generation procedure reduces to the NP-hard rectangular maximum agreement (RMA) problem of finding a box that best discriminates between two weighted datasets. We solve this problem exactly using a parallel branch-and-bound procedure. The resulting rule-enhanced regression procedure is computation-intensive, but has promising prediction performance.
Cite
Text
Eckstein et al. "Rule-Enhanced Penalized Regression by Column Generation Using Rectangular Maximum Agreement." International Conference on Machine Learning, 2017.Markdown
[Eckstein et al. "Rule-Enhanced Penalized Regression by Column Generation Using Rectangular Maximum Agreement." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/eckstein2017icml-ruleenhanced/)BibTeX
@inproceedings{eckstein2017icml-ruleenhanced,
title = {{Rule-Enhanced Penalized Regression by Column Generation Using Rectangular Maximum Agreement}},
author = {Eckstein, Jonathan and Goldberg, Noam and Kagawa, Ai},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {1059-1067},
volume = {70},
url = {https://mlanthology.org/icml/2017/eckstein2017icml-ruleenhanced/}
}