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Bioinformatics Advance Access originally published online on July 22, 2008
Bioinformatics 2008 24(18):2010-2014; doi:10.1093/bioinformatics/btn356
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Enriched random forests

Dhammika Amaratunga 1,*, Javier Cabrera 2 and Yung-Seop Lee 3

1Department of Nonclinical Biostatistics, Johnson & Johnson PRD LLC, Raritan, NJ 08869, 2Department of Statistics, Rutgers University, 110 Frelinghuysen Ave, Piscataway, NJ 08854, USA and 3Department of Statistics, Dongguk University, Seoul, South Korea

*To whom correspondence should be addressed.


  Abstract

Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly. In such instances, the procedure can be improved by reducing the contribution of trees whose nodes are populated by non-informative features. To some extent, this can be achieved by prefiltering, but we propose a novel, yet simple, adjustment that has demonstrably superior performance: choose the eligible subsets at each node by weighted random sampling instead of simple random sampling, with the weights tilted in favor of the informative features. This results in an ‘enriched random forest’. We illustrate the superior performance of this procedure in several actual microarray datasets.

Contact: damaratu{at}prdus.jnj.com

Associate Editor: David Rocke

Received on July 1, 2008; revised on July 1, 2008; accepted on July 10, 2008

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