MLUL: Multi-label undersampling based on local label imbalance...

In mldr.resampling: Resampling Algorithms for Multi-Label Datasets

Multi-label undersampling based on local label imbalance (MLUL)

Description

This function implements the MLUL algorithm. It is a preprocessing algorithm for imbalanced multilabel datasets, which applies undersampling, removing difficult instances according to their neighbors.

Usage

MLUL(D, P, k, neighbors = NULL, tableVDM = NULL)

Arguments

D	mld mldr object with the multilabel dataset to preprocess
P	Percentage in which the original dataset is decreased
k	Number of neighbors to be considered when computing the neighbors of an instance
neighbors	Structure with all instances and neighbors in the dataset. If it is empty, it will be calculated by the function
tableVDM	Dataframe object containing previous calculations for faster processing. If it is empty, the algorithm will be slower

Value

A mld object containing the preprocessed multilabel dataset

Source

Liu, B., Blekas, K., & Tsoumakas, G. (2022). Multi-label sampling based on local label imbalance. Pattern Recognition, 122, 108294.

mldr.resampling documentation built on Aug. 22, 2023, 5:11 p.m.