MLRkNNOS: Reverse-nearest neighborhood based oversampling for...

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

Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets

Description

This function implements an algorithm that uses the concept of reverse nearest neighbors, in order to create new instances for each label. Then, several radial SVMs, one for each label, are trained in order to predict each label of the synthetic instances.

Usage

MLRkNNOS(D, k, 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
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

Sadhukhan, P., & Palit, S. (2019). Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets. Pattern Recognition Letters, 125, 813-820

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