R package varSelRF: Variable selection using random forests. 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). You can use rpvm instead of Rmpi if you want but I’ve only tested with Rmpi.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
- Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
- Gilles Kratzer, Reinhard Furrer: varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets (2018) arXiv
- Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot: VSURF: An R Package for Variable Selection Using Random Forests (2015) not zbMATH
- Miron Kursa; Witold Rudnicki: Feature Selection with the Boruta Package (2010) not zbMATH
- Siroky, David S.: Navigating random forests and related advances in algorithmic modeling (2009)
- Tierney, Luke; Rossini, A. J.; Li, Na: \textttSnow: A parallel computing framework for the R system (2009)
- Diaz-Uriarte, Ramón: Genesrf and varselrf: a web-based tool and R package for gene selection and classification using random forest (2007) ioport