MLSOL

This function implements the MLSOL algorithm. It is a preprocessing algorithm for imbalanced multilabel datasets, which applies oversampling on difficult regions of the instance space, in order to help classifiers distinguish 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

MLSOL function

<dl><dt>D</dt>
<dd>mld <code>mldr</code> object with the multilabel dataset to preprocess</dd>
<dt>P</dt>
<dd>Percentage in which the original dataset is increased</dd>
<dt>k</dt>
<dd>Number of neighbors to be considered when computing the neighbors of an instance</dd>
<dt>neighbors</dt>
<dd>Structure with all instances and neighbors in the dataset. 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

Multi-label oversampling based on local label imbalance (MLSOL) — MLSOL

<dl>

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


<dt>P</dt>
<dd>Percentage in which the original dataset is increased</dd>


<dt>k</dt>
<dd>Number of neighbors to be considered when computing the neighbors of an instance</dd>


<dt>neighbors</dt>
<dd>Structure with all instances and neighbors in the dataset. 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>

MLSOL: Multi-label oversampling based on local label imbalance (MLSOL)

Description

Usage

Value

Arguments