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A new variable selection approach using Random Forests

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  • Hapfelmeier, A.
  • Ulm, K.
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    Abstract

    Random Forests are frequently applied as they achieve a high prediction accuracy and have the ability to identify informative variables. Several approaches for variable selection have been proposed to combine and intensify these qualities. An extensive review of the corresponding literature led to the development of a new approach that is based on the theoretical framework of permutation tests and meets important statistical properties. A comparison to another eight popular variable selection methods in three simulation studies and four real data applications indicated that: the new approach can also be used to control the test-wise and family-wise error rate, provides a higher power to distinguish relevant from irrelevant variables and leads to models which are located among the very best performing ones. In addition, it is equally applicable to regression and classification problems.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312003490
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 60 (2013)
    Issue (Month): C ()
    Pages: 50-69

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    Handle: RePEc:eee:csdana:v:60:y:2013:i:c:p:50-69

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Random Forests; Variable selection; Permutation tests; Multiple testing;

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    Cited by:
    1. Fellinghauer, Bernd & B├╝hlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.

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