ICML 2017poster9 citations
Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement
Jonathan Eckstein, Noam Goldberg, Ai Kagawa
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.
BibTeX
@InProceedings{pmlr-v70-eckstein17a,
title = {Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement},
author = {Jonathan Eckstein and Noam Goldberg and Ai Kagawa},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {1059--1067},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/eckstein17a/eckstein17a.pdf},
url = {https://proceedings.mlr.press/v70/eckstein17a.html},
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.}
}