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

An imbalanced dataset with a minor class centered around the origin with a majority class surrounding the center.

a data.frame with 1000 rows and 3 columns.

<a href="https://gist.github.com/s-kganz/c2534666e369f8e19491bb29d53c619d">https://gist.github.com/s-kganz/c2534666e369f8e19491bb29d53c619d</a>

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

bullseye

Description

Usage

Format

Source

scutr

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