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

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.

Package details

AuthorMattia Chiesa <mattia.chiesa@hotmail.it>, Luca Piacentini <luca.piacentini@cardiologicomonzino.it>
Bioconductor views Classification Clustering FeatureExtraction
MaintainerMattia Chiesa <mattia.chiesa@hotmail.it>
LicenseGPL (>= 2)
Version1.8.0
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("GARS")

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GARS documentation built on April 29, 2020, 2:14 a.m.