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 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

Documentation:

Reference manual:

scutr.html , scutr.pdf

Downloads:

Package source:	scutr_0.2.0.tar.gz
Windows binaries:	r-devel: scutr_0.2.0.zip, r-release: scutr_0.2.0.zip, r-oldrel: scutr_0.2.0.zip
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

Reverse dependencies:

Reverse imports:

MantaID

Linking:

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