scutr: Balancing Multiclass Datasets for Classification Tasks

Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.

Version: 0.1.2
Depends: R (≥ 2.10)
Imports: smotefamily, parallel, mclust
Suggests: testthat (≥ 2.0.0)
Published: 2021-06-24
Author: Keenan Ganz [aut, cre]
Maintainer: Keenan Ganz <ganzkeenan1 at gmail.com>
BugReports: https://github.com/s-kganz/scutr/issues
License: MIT + file LICENSE
URL: https://github.com/s-kganz/scutr
NeedsCompilation: no
Materials: README NEWS
CRAN checks: scutr results

Downloads:

Reference manual: scutr.pdf
Package source: scutr_0.1.2.tar.gz
Windows binaries: r-devel: scutr_0.1.2.zip, r-release: scutr_0.1.2.zip, r-oldrel: scutr_0.1.2.zip
macOS binaries: r-release (arm64): scutr_0.1.2.tgz, r-release (x86_64): scutr_0.1.2.tgz, r-oldrel: scutr_0.1.2.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=scutr to link to this page.