This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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356
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #307130 |
Title:
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Thinning Random Forests
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Author(s):
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Jie Xu* and J. Brian Gray+
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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 Info Systems, Statistics, and Mgt Science, Tuscaloosa, AL, 35487-0226,
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Keywords:
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cluster analysis ;
ensemble model ;
machine learning ;
predictive modeling
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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. However, an ensemble model typically consists of hundreds of single classifiers, which makes model interpretation difficult. In this presentation, we propose a new method based on cluster analysis for significantly reducing the size of an ensemble while maintaining roughly the same predictive accuracy. Model interpretation is improved, and computation time for future predictions is reduced. The method is demonstrated on several data sets and compared to other ensemble methods.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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