getC

Auxiliary function used by MLSOL and MLUL. For each instance in the dataset, we compute, for each label, the proportion of neighbors having an opposite class with respect to the proper instance

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

getC 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>neighbors</dt>
<dd>Structure with the neighbors of every instance in the dataset</dd>
<dt>k</dt>
<dd>Number of neighbors taken into account for each instance</dd></dl>

Arguments

Auxiliary function used by MLSOL and MLUL. For each instance in the dataset, we compute, for each label, the proportion of neighbors having an opposite class with respect to the proper instance — getC

<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>neighbors</dt>
<dd>Structure with the neighbors of every instance in the dataset</dd>


<dt>k</dt>
<dd>Number of neighbors taken into account for each instance</dd>

</dl>

Auxiliary function used by MLSOL and MLUL. For each instance in the dataset, we compute, for each label, the proportion of neighbors having an opposite class with respect to the proper instance

getC: Auxiliary function used by MLSOL and MLUL. For each instance in the dataset, we compute, for each label, the proportion of neighbors having an opposite class with respect to the proper instance

Description

Usage

Value

Arguments