Balancing Multiclass Datasets for Classification Tasks
Oversample a dataset by SMOTE.
Randomly resample a dataset.
Stratified index sample of different values in a vector.
SMOTE and cluster-based undersampling technique.
Undersample a dataset by hierarchical clustering.
Undersample a dataset by kmeans clustering.
Undersample a dataset by expectation-maximization clustering
Undersample a dataset by iteratively removing the observation with the...
Undersample a dataset by removing Tomek links.
Validate a dataset for resampling.
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.