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

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Nicodemus, Kristin K ^a,b,c   Malley, James D ^d   Strobl, Carolin ^e   Ziegler, Andreas ^f  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

^d NATIONAL INSTITUTES OF HEALTH   (United States)

^e UNIVERSITY OF MUNICH   (Germany)

^f UNIVERSITY HOSPITAL SCHLESWIG HOLSTEIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

GENERATING MODELS; GENOME-WIDE ASSOCIATION; LARGE DATASETS; NULL HYPOTHESIS; RANDOM FORESTS; SIMULATION STUDIES; SPURIOUS SIGNALS; VARIABLE IMPORTANCES;

DECISION TREES;

ALGORITHM; ARTICLE; GENETIC ASSOCIATION; GENOME; GENOMICS; METHODOLOGY;

ALGORITHMS; GENOME; GENOME-WIDE ASSOCIATION STUDY; GENOMICS;

EID: 77949388276     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-110     Document Type: Article

References (11)

1. Breiman L. Random forests. Machine Learn 2001, 45:5-32.
2. Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. J Comp Graph Stat 2006, 15:651-674.
3. Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random forests. BMC Bioinformatics 2008, 9:307. 10.1186/1471-2105-9-307, 2491635, 18620558.
4. Nicodemus KK, Malley JD. Predictor correlation impacts machine learning algorithms: implications for genomic studies. Bioinformatics 2009, 25(15):1884-90. 10.1093/bioinformatics/btp331, 19460890.
5. Meng Y, Yu Y, Cupples LA, Farrer LA, Lunetta KL. Performance of random forest when SNPs are in linkage disequilibrium. BMC Bioinformatics 2009, 10:78. 10.1186/1471-2105-10-78, 2666661, 19265542.
6. Díaz-Uriarte R, Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006, 7:3. 10.1186/1471-2105-7-3, 1363357, 16398926.
7. Strobl C, Zeileis A. Exploring the statistical properties of a test for random forest variable importance. COMPSTAT 2008 - Proceedings in Computational Statistics 2008, II:59-66. Physica Verlag, Heidelberg.
8. Cordell HJ. Genome-wide association studies: Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 2009, 10:392-404.
9. Liaw A, Weiner M. Classification and regression by randomForest. R News 2002, 2:18-22.
10. R Development Core Team R: A language and environment for statistical computing. 2007, R Foundation for statistical computing, Vienna, Austria, R Development Core Team.
11. Kendall M, Stuart A. The Advanced Theory of Statistics 1979, Griffin: London.