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Abstract Details
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Activity Number:
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658
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304715 |
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Title:
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A Comparison of Strategies to Handle Missing Data in Random Forests
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Author(s):
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Ren He and Christina Ramirez Kitchen and Anna Liza Antonio*+
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Companies:
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University of California at Los Angeles Fielding School of Public Health and University of California at Los Angeles Fielding School of Public Health and University of California at Los Angeles Fielding School of Public Health
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Address:
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Department of Biostatistics, Los Angeles, CA, 90095, United States
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Keywords:
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Abstract:
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In recent years, Random Forests have become a very popular tool used in various fields of research (e.g. bioinformatics, financial services and pattern recognition). The attractiveness of this algorithm lies in its ability to deal with high-dimensional problems which include complex interaction effects. However, missing values may affect the efficiency of Random Forests. In this paper, we compare several strategies to handle missing data under a variety of simulation settings, which include different missing data mechanisms, missing rates and correlation structures. The simulation results are presented in this paper.
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