getU

Auxiliary function used by MLUL. It computes the influence of each instance with respect to its reverse 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

getU function

<dl><dt>D</dt>
<dd>mld <code>mldr</code> object with the multilabel dataset to preprocess</dd>
<dt>d</dt>
<dd>Vector with the instances of the dataset which have one or more label active (ideally, all of them)</dd>
<dt>rNeighbors</dt>
<dd>Structure with the reverse nearest neighbors of each instance of the dataset</dd>
<dt>S</dt>
<dd>Structure with the proportion of neighbors having an opposite class with respect to an instance and label, normalized by the global class imbalance</dd></dl>

Arguments

Auxiliary function used by MLUL. It computes the influence of each instance with respect to its reverse neighbors — getU

<dl>

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


<dt>d</dt>
<dd>Vector with the instances of the dataset which have one or more label active (ideally, all of them)</dd>


<dt>rNeighbors</dt>
<dd>Structure with the reverse nearest neighbors of each instance of the dataset</dd>


<dt>S</dt>
<dd>Structure with the proportion of neighbors having an opposite class with respect to an instance and label, normalized by the global class imbalance</dd>

</dl>

getU: Auxiliary function used by MLUL. It computes the influence of each instance with respect to its reverse neighbors

Description

Usage

Value

Arguments