MLeNN: Multilabel edited Nearest Neighbor (MLeNN)

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

Multilabel edited Nearest Neighbor (MLeNN)

Description

This function implements the MLeNN algorithm. It is a preprocessing algorithm for imbalanced multilabel datasets, whose aim is to identify instances with majoritary labels, and remove its neihgbors which are too different to them, in terms of active labels.

Usage

MLeNN(D, TH = 0.5, k = 3, neighbors = NULL, tableVDM = NULL)

Arguments

D	mld mldr object with the multilabel dataset to preprocess
TH	threshold for the Hamming Distance in order to consider an instance different to another one. Defaults to 0.5.
k	number of nearest neighbours to check for each instance. Defaults to 3.
neighbors	Structure with instances and neighbors. 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

An mldr object containing the preprocessed multilabel dataset

Source

Francisco Charte, Antonio J. Rivera, María J. del Jesus, and Francisco Herrera. MLeNN: A First Approach to Heuristic Multilabel Undersampling. Intelligent Data Engineering and Automated Learning – IDEAL 2014. ISBN 978-3-319-10840-7.

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