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Activity Number: 336
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #306504
Title: Mathematical Programming Techniques for Improving Ensemble Models
Author(s): J. Brian Gray*+ and Jie Xu
Companies: The University of Alabama and The University of Alabama
Address: Dept of ISM, Statistics, and Mgt Science, Tuscaloosa, AL, 35487-0226, United States
Keywords: bagging ; boosting ; machine learning ; predictive modeling ; random forests
Abstract:

Ensemble models, such as bagging, random forests, and boosting, have better predictive accuracy than single classifiers. These ensembles typically consist of hundreds of single classifiers, which makes model interpretation and future prediction computations much more difficult than for single classifiers. In this presentation, we describe several mathematical programming techniques, involving linear, quadratic, and binary integer programming, for reducing the size of an ensemble model while maintaining, and in some cases improving, the predictive performance of the ensemble.


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