newSample

Auxiliary function used by MLSMOTE. Creates a synthetic sample based on values of attributes and labels of its neighbors

Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.

Miguel Ángel Dávila

mldr.resampling

Resampling Algorithms for Multi-Label Datasets

Francisco Charte

María José Del Jesus

Antonio Rivera

newSample function

<dl><dt>seedInstance</dt>
<dd>Sample we are using as "template"</dd>
<dt>refNeigh</dt>
<dd>Reference neighbor</dd>
<dt>neighbors</dt>
<dd>Neighbors to take into account</dd>
<dt>strategy</dt>
<dd>Strategy for choosing the synthetic labels: union, intersection or ranking</dd>
<dt>D</dt>
<dd>mld <code>mldr</code> object with the multilabel dataset to preprocess</dd></dl>

Arguments

Auxiliary function used by MLSMOTE. Creates a synthetic sample based on values of attributes and labels of its neighbors — newSample

<dl>

<dt>seedInstance</dt>
<dd>Sample we are using as "template"</dd>


<dt>refNeigh</dt>
<dd>Reference neighbor</dd>


<dt>neighbors</dt>
<dd>Neighbors to take into account</dd>


<dt>strategy</dt>
<dd>Strategy for choosing the synthetic labels: union, intersection or ranking</dd>


<dt>D</dt>
<dd>mld <code>mldr</code> object with the multilabel dataset to preprocess</dd>

</dl>

newSample: Auxiliary function used by MLSMOTE. Creates a synthetic sample based on values of attributes and labels of its neighbors

Description

Usage

Value

Arguments