Abstract Details
Activity Number:
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409
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 2:45 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #314077
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Title:
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Novel Methods to Identify and Estimate Interactions via Random Forest
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Author(s):
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Arturo Valdivia*+
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Companies:
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Keywords:
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Random Forest ;
Interactions ;
Statistical Learning ;
Data Mining
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Abstract:
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This work proposes two heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The study develops two novel interaction measures. The first measure is constructed to identify interactions in a general setting where a model specification is not assumed in advance. The second measure is built to identify and estimate interactions when the model specification is assumed to be linear. Both measures are unique in their construction; they take into account not only the outcome values, but also the internal structure of the trees in a random forest. In a simulation study, under a variety of conditions, the proposed measures are found to identify and estimate higher-order interactions. Potential applications of these methods are also presented.
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Authors who are presenting talks have a * after their name.
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