rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery

• Main Authors: Michał Dramiński, Jacek Koronacki
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2018-07-01
• Series: Journal of Statistical Software
• Subjects: MCFS-ID; feature selection; high-dimensional problems; Java; R; ID graph
• Online Access: https://www.jstatsoft.org/index.php/jss/article/view/2621

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.