Package: scutr 0.2.0
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.
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NEWS
# Install 'scutr' in R: |
install.packages('scutr', repos = c('https://s-kganz.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/s-kganz/scutr/issues
- bullseye - An imbalanced dataset with a minor class centered around the origin with a majority class surrounding the center.
- imbalance - An imbalanced dataset with randomly placed normal distributions around the origin. The nth class has n * 10 observations.
- wine - Type and chemical analysis of three different kinds of wine.
Last updated 12 months agofrom:624f415cd4. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 04 2024 |
R-4.5-win | OK | Nov 04 2024 |
R-4.5-linux | OK | Nov 04 2024 |
R-4.4-win | OK | Nov 04 2024 |
R-4.4-mac | OK | Nov 04 2024 |
R-4.3-win | OK | Nov 04 2024 |
R-4.3-mac | OK | Nov 04 2024 |
Exports:oversample_smoteresample_randomsample_classesSCUTSCUT_parallelundersample_hclustundersample_kmeansundersample_mclustundersample_mindistundersample_tomek
Dependencies:clicpp11dbscanFNNgenericsglueigraphlatticelifecyclemagrittrMatrixmclustpkgconfigRcpprlangsmotefamilyvctrs