Enriched random forests

Dhammika Amaratunga, Javier Cabrera, Yung Seop Lee

Research output: Contribution to journalArticlepeer-review

114 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2010-2014
Number of pages5
JournalBioinformatics
Volume24
Issue number18
DOIs
StatePublished - Sep 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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