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Feature Selection with the Boruta Package
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DOI：10.18637/jss.v036.i11.png)
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摘要
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This article describes a R package Boruta, implementing a novel feature selection algorithm for finding all relevant variables. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.
Keyword:
feature selection
feature ranking
random forest
