MLSMOTE: Synthetic oversampling of multilabel instances (MLSMOTE)

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

Synthetic oversampling of multilabel instances (MLSMOTE)

Description

This function implements the MLSMOTE algorithm. It is a preprocessing algorithm for imbalanced multilabel datasets, whose aim is to identify instances with minoritary labels, and generate synthetic instances based on their neighbor instances.

Usage

MLSMOTE(D, k, strategy = "ranking", tableVDM = NULL)

Arguments

D	mld mldr object with the multilabel dataset to preprocess
k	Number of neighbors to be considered when creating a synthetic instance
strategy	Strategy for choosing the synthetic labels. Possible values: "union", "intersection" and "ranking" (default)
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

Charte, F., Rivera, A. J., del Jesus, M. J., & Herrera, F. (2015). MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. Knowledge-Based Systems, 89, 385-397.

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