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sigFeature

sigFeature: Significant feature selection using SVM-RFE & t-statistic

Bioconductor version: 3.23 · Package version: 1.30.0

This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("sigFeature")

Details

MaintainerPijush Das Developer <topijush@gmail.com>
AuthorPijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb]
LicenseGPL (>= 2)
Downloads rank467
Source branchRELEASE_3_23
biocViewsClassification, FeatureExtraction, GeneExpression, GenePrediction, Microarray, Normalization, Software, SupportVectorMachine, Transcription, mRNAMicroarray

Documentation

Download

Dependencies

Depends: R (>= 3.5.0)

Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods

Suggests: RUnit, BiocGenerics, knitr, rmarkdown