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.

Balancing Multiclass Datasets for Classification Tasks

package-ubuntu-20.04-r-4.0.5#cran#scutr-0.1.2

Type and chemical analysis of three different kinds of wine.

a data.frame with 178 rows and 14 columns

<a href="https://archive.ics.uci.edu/ml/datasets/Wine">https://archive.ics.uci.edu/ml/datasets/Wine</a>

package-ubuntu-20.04-r-4.0.5#cran#scutr-0.1.2#docs#wine

wine

Description

Usage

Format

Source

scutr

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