mRMRe

Parallelized Minimum Redundancy, Maximum Relevance (mRMR) Ensemble Feature Selection

Description: Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique.
Authors: Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen, Gianluca Bontempi, Benjamin Haibe-Kains
Lab: Haibe-Kains
Version: 2.1.0
Keywords: mRMRe, mutual information matrices, feature selection, mRMR, RadioGx
Licensing: Artistic-2.0

Citation

De Jay, N., Papillon-Cavanagh, S., Olsen, C., El-Hachem, N., Bontempi, G., & Haibe-Kains, B. (2013). mRMRe: an R package for parallelized mRMR ensemble feature selection. Bioinformatics, 29(18), 2365-2368.
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