MLSOL: Multi-label oversampling based on local label imbalance...

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

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

Description

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.

Usage

MLSOL(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 increased
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.