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: 0.2.0
Depends: R (≥ 2.10)
Imports: smotefamily, parallel, mclust
Suggests: testthat (≥ 2.0.0)
Published: 2023-11-17
DOI: 10.32614/CRAN.package.scutr
Author: Keenan Ganz [aut, cre]
Maintainer: Keenan Ganz <ganzkeenan1 at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: scutr results


Reference manual: scutr.pdf


Package source: scutr_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): scutr_0.2.0.tgz, r-oldrel (arm64): scutr_0.2.0.tgz, r-release (x86_64): scutr_0.2.0.tgz, r-oldrel (x86_64): scutr_0.2.0.tgz
Old sources: scutr archive


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