The behaviour of random forest permutation-based variable importance measures under predictor correlation期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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The behaviour of random forest permutation-based variable importance measures under predictor correlation

Authors:	Kristin K Nicodemus James D Malley Carolin Strobl Andreas Ziegler

Institution:	1.Statistical Genetics, Wellcome Trust Centre for Human Genetics,University of Oxford,Oxford,UK;2.Department of Clinical Pharmacology,University of Oxford,Oxford,UK;3.Genes, Cognition and Psychosis Program, Intramural Research Program, National Institute of Mental Health,National Institutes of Health,Bethesda,USA;4.Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience,Center for Information Technology, National Institutes of Health,Bethesda,USA;5.Department für Statistik,Ludwig-Maximilians Universit?t München,München,Germany;6.Institut für Medizinische Biometrie und Statistik, Universit?t zu Lübeck,Universit?tsklinikum Schleswig-Holstein,Lübeck,Germany

Abstract:	Background Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results.

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