Abstract Details
Activity Number:
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246
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #313845
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Title:
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Robust VIF Regression: A SAS Application to Feature Selection in Large Data Sets
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Author(s):
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Ruiwen Zhang*+
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Companies:
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SAS Institute
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Keywords:
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VIF regression ;
streamwise regression ;
feature selection ;
SAS
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Abstract:
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Streamwise regression and its application to feature selection has its advantages over traditional stepwise regression methods as it offers faster computational speed and also handles big number of features streamwise as data sets now can easily contains huge number of potential variables, especially from areas as genetic sequencing, sensor network, finance related fields, and etc. A recently propose streamwise regression approach based on the variance inflation factor (VIF) and fast robust estimators has been proven to be an efficient algorithm in finding good subsets of variables from a huge space of candidates. Moreover, with the robust VIF regression, overfitting can be controlled by dynamically adjusting the threshold for adding features to the model. We implement the algorithm using SAS macros and provide comprehensive examples so that SAS users can get benefits from SAS platform or server which usually stores their big data sets and also from the robust VIF regression, a much-needed robust feature selection for large data sets.
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Authors who are presenting talks have a * after their name.
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