Abstract
We describe a procedure enhancing Lx-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 AAP-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 method is computation-intensive, but has promising prediction performance.
Original language | English |
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Title of host publication | 34th International Conference on Machine Learning, ICML 2017 |
Publisher | International Machine Learning Society (IMLS) |
Pages | 1762-1770 |
Number of pages | 9 |
ISBN (Electronic) | 9781510855144 |
State | Published - 2017 |
Event | 34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia Duration: 6 Aug 2017 → 11 Aug 2017 |
Publication series
Name | 34th International Conference on Machine Learning, ICML 2017 |
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Volume | 3 |
Conference
Conference | 34th International Conference on Machine Learning, ICML 2017 |
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Country/Territory | Australia |
City | Sydney |
Period | 6/08/17 → 11/08/17 |
Bibliographical note
Publisher Copyright:Copyright 2017 by the author(s).