undersample_mindist

Undersample a dataset by iteratively removing the observation with the lowest total distance to its neighbors of the same class.

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_mindist function

<dl><dt>data</dt>
<dd>Dataset to undersample. Aside from <code>cls_col</code>, must be numeric.</dd>
<dt>cls</dt>
<dd>Class to be undersampled.</dd>
<dt>cls_col</dt>
<dd>Column containing class information.</dd>
<dt>m</dt>
<dd>Desired number of observations after undersampling.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/dist?package=scutr&version=0.2.0" data-mini-rdoc="scutr::dist">dist</a>()</code>.</dd></dl>

Arguments

Undersample a dataset by iteratively removing the observation with the lowest total distance to its neighbors of the same class. — undersample_mindist

<dl>

<dt>data</dt>
<dd>Dataset to undersample. Aside from <code>cls_col</code>, must be numeric.</dd>


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


<dt>cls_col</dt>
<dd>Column containing class information.</dd>


<dt>m</dt>
<dd>Desired number of observations after undersampling.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/r/stats/dist.html'>dist</a>()</code>.</dd>

</dl>

undersample_mindist: Undersample a dataset by iteratively removing the observation with the lowest total distance to its neighbors of the same class.

Description

Usage

Value

Arguments

Examples