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