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This is the development version of GARS; for the stable release version, see GARS.

GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Bioconductor version: Development (3.20)

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.

Author: Mattia Chiesa <mattia.chiesa at>, Luca Piacentini <luca.piacentini at>

Maintainer: Mattia Chiesa <mattia.chiesa at>

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GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets PDF R Script
Reference Manual PDF


biocViews Classification, Clustering, FeatureExtraction, Software
Version 1.25.0
In Bioconductor since BioC 3.7 (R-3.5) (6 years)
License GPL (>= 2)
Depends R (>= 3.5), ggplot2, cluster
Imports DaMiRseq, MLSeq, stats, methods, SummarizedExperiment
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