Feature Selection with the Boruta Package
Miron B. Kursa, Witold R. Rudnicki
This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{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.
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What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Random Forests | 2001 | 131,109 |
| Classification and Regression by randomForest | 2007 | 18,420 |
| Wrappers for feature subset selection | 1997 | 8,991 |
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| Neural Networks and Applications | Computer Science |
| Machine Learning and Data Classification | Computer Science |
| Face and Expression Recognition | Computer Science |
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