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 <[email protected]>, Luca Piacentini <[email protected]>
Bioconductor views Classification Clustering FeatureExtraction
MaintainerMattia Chiesa <[email protected]>
LicenseGPL (>= 2)
Version1.2.0
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")

Try the GARS package in your browser

Any scripts or data that you put into this service are public.

GARS documentation built on Nov. 1, 2018, 2:55 a.m.