Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).
An R package for variable selection using random Forest.
Diaz-Uriarte and Alvarez de Andres, 2006, "Gene selection and classification of microarray data using random forest.", BMC Bioinformatics, 2006, 7:3 (with Supplementary Material)
Diaz-Uriarte, 2007, "GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest." BMC Bioinformatics, 8: 328.
GPL (>= 2)