MLeNN

This function implements the MLeNN algorithm. It is a preprocessing algorithm for imbalanced multilabel datasets,
whose aim is to identify instances with majoritary labels, and remove its neihgbors which are too different to them, in terms of active labels.

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

MLeNN function

<dl><dt>D</dt>
<dd>mld <code>mldr</code> object with the multilabel dataset to preprocess</dd>
<dt>TH</dt>
<dd>threshold for the Hamming Distance in order to consider an instance different to another one. Defaults to 0.5.</dd>
<dt>k</dt>
<dd>number of nearest neighbours to check for each instance. Defaults to 3.</dd>
<dt>neighbors</dt>
<dd>Structure with instances and neighbors. If it is empty, it will be calculated by the function</dd>
<dt>tableVDM</dt>
<dd>Dataframe object containing previous calculations for faster processing. If it is empty, the algorithm will be slower</dd></dl>

Arguments

Multilabel edited Nearest Neighbor (MLeNN) — MLeNN

<dl>

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


<dt>TH</dt>
<dd>threshold for the Hamming Distance in order to consider an instance different to another one. Defaults to 0.5.</dd>


<dt>k</dt>
<dd>number of nearest neighbours to check for each instance. Defaults to 3.</dd>


<dt>neighbors</dt>
<dd>Structure with instances and neighbors. If it is empty, it will be calculated by the function</dd>


<dt>tableVDM</dt>
<dd>Dataframe object containing previous calculations for faster processing. If it is empty, the algorithm will be slower</dd>

</dl>

MLeNN: Multilabel edited Nearest Neighbor (MLeNN)

Description

Usage

Value

Arguments