{ "_id": "6a1ad18a1d7bb097a09e2672", "Package": "GARS", "Type": "Package", "Date": "2020-09-04", "Title": "GARS: Genetic Algorithm for the identification of Robust Subsets\nof variables in high-dimensional and challenging datasets", "Version": "1.33.0", "Author": "Mattia Chiesa , Luca Piacentini\n", "Maintainer": "Mattia Chiesa ", "Description": "Feature selection aims to identify and remove redundant,\nirrelevant and noisy variables from high-dimensional datasets.\nSelecting informative features affects the subsequent\nclassification and regression analyses by improving their\noverall performances. Several methods have been proposed to\nperform feature selection: most of them relies on univariate\nstatistics, correlation, entropy measurements or the usage of\nbackward/forward regressions. Herein, we propose an efficient,\nrobust and fast method that adopts stochastic optimization\napproaches for high-dimensional. GARS is an innovative\nimplementation of a genetic algorithm that selects robust\nfeatures in high-dimensional and challenging datasets.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "6.1.1", "biocViews": "Classification, FeatureExtraction, Clustering", "Config/pak/sysreqs": "cmake libglpk-dev make default-jdk libbz2-dev\nlibicu-dev libjpeg-dev liblzma-dev libpng-dev libuv1-dev\nlibxml2-dev libssl-dev xz-utils zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:47:23 UTC", "RemoteUrl": "https://github.com/bioc/GARS", "RemoteRef": "HEAD", "RemoteSha": "b89d2be6409b9731a436779ff9d70508c9bde095", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-30 08:26:09 UTC", "User": "root" }, "MD5sum": "c40ffe75475440ecadc47ac0f80fe015", "_user": "bioc", "_type": "src", "_file": "GARS_1.33.0.tar.gz", "_fileid": "fc9f24e7957ea75b90dda4f7a856648e90e55c102f90e0d8ba58e246e0766155", 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