Boosting classifiers with tightened L<inf>0</inf>-relaxation penalties

Noam Goldberg, Jonathan Eckstein

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the sparsity of the weight vector. In order to justify our optimization formulations, we first consider a novel integer linear program as a model for sparse classifier selection, generalizing the minimum disagreement half-space problem whose complexity has been investigated in computational learning theory. Specifically, our mixed integer problem is that of finding a separating hyper-plane with minimum empirical error subject to an L0-norm penalty. We note that common "soft margin" linear programming formulations for robust classification are equivalent to the continuous relaxation of our formulation. Since the initial continuous relaxation is weak, we suggest a tighter relaxation, using novel cutting planes, to better approximate the integer solution. To solve this relaxation, we propose a new boosting algorithm based on linear programming with dynamic generation of variables and constraints. We demonstrate the classification performance of our proposed algorithm with experimental results, and justify our selection of parameters using a minimum description length, compression interpretation of learning. Copyright 2010 by the author(s)/owner(s).
Original languageEnglish
JournalICML 2010 - Proceedings, 27th International Conference on Machine Learning
StatePublished - 17 Sep 2010

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