Analysis of a random forests model
The Journal of Machine Learning Research
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces
International Journal of Data Warehousing and Mining
THE NEW HYBRID METHOD FOR CLASSIFICATION OF PATIENTS BY GENE EXPRESSION PROFILING
Journal of Integrated Design & Process Science
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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@prdus.jnj.com