recipe bioconductor-gars

Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/GARS.html

License

GPL (>= 2)

Recipe

/bioconductor-gars/meta.yaml

package bioconductor-gars

(downloads) docker_bioconductor-gars

Versions

1.2.0-0

Depends
Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-gars

and update with:

conda update bioconductor-gars

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-gars:<tag>

(see bioconductor-gars/tags for valid values for <tag>)