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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 |
| 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 |
| Reference manual: | scutr.pdf |
| 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 imports: | MantaID |
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