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Abstract Details
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Activity Number:
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172
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300822 |
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Title:
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Robustness of Random Forests for Regression
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Author(s):
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Denis Larocque*+ and Marie-Helene Roy
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Companies:
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HEC Montréal and HEC Montréal
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Address:
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, , ,
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Keywords:
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Random forest ;
Robustness ;
Median ;
Ranks
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Abstract:
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In this paper, we empirically investigate the robustness of random forests for regression problems. We also investigate the performance of five variations of the original random forest method, all aimed at improving robustness. All the proposed variations can be easily implemented using the R package randomForest. The competing methods are compared via a simulation study and ten real data sets obtained from the UCI Machine Learning Repository. Our results show that the median-based random forests offer good and stable performances for the simulated and real data sets considered and, as such, should be considered as serious alternatives to the original random forest method.
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