undersample_mclust

Undersample a dataset by expectation-maximization clustering

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.

Keenan Ganz

scutr

Balancing Multiclass Datasets for Classification Tasks

undersample_mclust function

<dl><dt>data</dt>
<dd>Data to be undersampled.</dd>
<dt>cls</dt>
<dd>Class to be undersampled.</dd>
<dt>cls_col</dt>
<dd>Class column.</dd>
<dt>m</dt>
<dd>Number of samples in undersampled dataset.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>Mclust()</code></dd></dl>

Arguments

Undersample a dataset by expectation-maximization clustering — undersample_mclust

<dl>

<dt>data</dt>
<dd>Data to be undersampled.</dd>


<dt>cls</dt>
<dd>Class to be undersampled.</dd>


<dt>cls_col</dt>
<dd>Class column.</dd>


<dt>m</dt>
<dd>Number of samples in undersampled dataset.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>Mclust()</code></dd>

</dl>

undersample_mclust: Undersample a dataset by expectation-maximization clustering

Description

Usage

Value

Arguments

Examples