A service provided by the WU Library and the WU IT-Services

Bias in random forest variable importance measures: Illustrations, sources and a solution

Strobl, Carolin and Boulesteix, Anne-Laure and Zeileis, Achim and Hothorn, Torsten (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8. p. 25. ISSN 1471-2105

This is the latest version of this item.

[img]
Preview
PDF
Download (388Kb) | Preview

Abstract

Background: Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Results: Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. Conclusion: We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research. (authors' abstract)

Item Type: Article
Additional Information: CS was supported by the German Research Foundation (DFG), collaborative research center 386 "Statistical Analysis of Discrete Structures". TH received financial support from DFG grant HO 3242/1-3. The authors would like to thank Thomas Augustin, Friedrich Leisch and Gerhard Tutz for fruitful discussions and for supporting our interest in this field of research, and Peter Bühlmann, an anonymous referee and a semi-anonymous referee for their helpful suggestions.
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Variance from Published Version: None
Depositing User: Elena Simukovic
Date Deposited: 12 Apr 2016 14:33
Last Modified: 19 Apr 2016 15:19
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/37957/
URI: http://epub.wu.ac.at/id/eprint/4983

Available Versions of this Item

Actions

View Item