Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Michał Dramiński, Jacek Koronacki
Title: rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery
Abstract: We describe the R package rmcfs that implements an algorithm for ranking features from high dimensional data according to their importance for a given supervised classification task. The ranking is performed prior to addressing the classification task per se. This R package is the new and extended version of the MCFS (Monte Carlo feature selection) algorithm where an early version was published in 2005. The package provides an easy R interface, a set of tools to review results and the new ID (interdependency discovery) component. The algorithm can be used on continuous and/or categorical features (e.g., gene expression and phenotypic data) to produce an objective ranking of features with a statistically well-defined cutoff between informative and non-informative ones. Moreover, the directed ID graph that presents interdependencies between informative features is provided.

Page views:: 2546. Submitted: 2016-03-30. Published: 2018-07-30.
Paper: rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery     Download PDF (Downloads: 1259)
Supplements:
rmcfs_1.2.13.tar.gz: R source package Download (Downloads: 177; 4MB)
v85i12.R: R replication code Download (Downloads: 158; 7KB)
arcene_train.data.rds: Replication data Download (Downloads: 98; 1MB)
arcene_valid.data.rds: Replication data Download (Downloads: 104; 1MB)
DOI: 10.18637/jss.v085.i12

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.