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) . 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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0.1.2 by Keenan Ganz, 5 months ago

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Authors: Keenan Ganz [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports smotefamily, parallel, mclust

Suggests testthat

See at CRAN