# mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms

arXiv: https://arxiv.org/abs/2305.17152 · HTML: https://mlsanity.com/paper/2305.17152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 6 of 6)

- [Multi-Label Sampling based on Local Label Imbalance](https://mlsanity.com/paper/2005.03240/) (2020)
- [Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets](https://mlsanity.com/paper/1802.05033/) (2018)
- [Synthetic Oversampling of Multi-Label Data based on Local Label Distribution](https://mlsanity.com/paper/1905.00609/) (2019)
- [Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises](https://mlsanity.com/paper/1501.04870/) (2015)
- [A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods](https://mlsanity.com/paper/1811.12044/) (2018)
- [Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository](https://mlsanity.com/paper/1802.03568/) (2018)

## Abstract (arXiv)

Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes limited to the pseudocode provided by their authors in a paper. This Original Software Publication presents mldr.resampling, a software package that provides reference implementations for eleven multilabel resampling methods, with an emphasis on efficiency since these algorithms are usually time-consuming.