R package VSURF: Variable Selection Using Random Forests. Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. and Poggi, J.M. and Tuleau-Malot, C. (2015) <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Mao, Xiaojun; Peng, Liuhua; Wang, Zhonglei: Nonparametric feature selection by random forests and deep neural networks (2022)
- Chavent, Marie; Genuer, Robin; Saracco, Jérôme: Combining clustering of variables and feature selection using random forests (2021)
- Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
- Lopes, Miles E.; Wu, Suofei; Lee, Thomas C. M.: Measuring the algorithmic convergence of randomized ensembles: the regression setting (2020)
- El Haouij, Neska; Poggi, Jean-Michel; Ghozi, Raja; Sevestre-Ghalila, Sylvie; Jaïdane, Mériem: Random forest-based approach for physiological functional variable selection for driver’s stress level classification (2019)
- Jlassi, Ines; Saracco, Jérôme: Variable importance assessment in sliced inverse regression for variable selection (2019)
- Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot: VSURF: An R Package for Variable Selection Using Random Forests (2015) not zbMATH