Rule-enhanced penalized regression by column generation using rectangular maximum agreement

Jonathan Eckstein, Noam Goldberg, Ai Kagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication34th International Conference on Machine Learning, ICML 2017
PublisherInternational Machine Learning Society (IMLS)
Pages1762-1770
Number of pages9
ISBN (Electronic)9781510855144
StatePublished - Jan 1 2017
Event34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia
Duration: Aug 6 2017Aug 11 2017

Publication series

Name34th International Conference on Machine Learning, ICML 2017
Volume3

Other

Other34th International Conference on Machine Learning, ICML 2017
Country/TerritoryAustralia
CitySydney
Period8/6/178/11/17

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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