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 <[email protected]>, Luca Piacentini <[email protected]>|
|Bioconductor views||Classification Clustering FeatureExtraction|
|Maintainer||Mattia Chiesa <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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