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.
|Author||Keenan Ganz [aut, cre]|
|Maintainer||Keenan Ganz <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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