# Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets

arXiv: https://arxiv.org/abs/1802.05033 · HTML: https://mlsanity.com/paper/1802.05033/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring](https://mlsanity.com/paper/2208.04227/) (2022)
- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning](https://mlsanity.com/paper/2302.13571/) (2023)
- [mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms](https://mlsanity.com/paper/2305.17152/) (2023)
- [A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data](https://mlsanity.com/paper/2411.01013/) (2024)
- [A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research](https://mlsanity.com/paper/2512.08371/) (2025)

## Abstract (arXiv)

Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In addition, this characteristic challenges many of the existent preprocessing algorithms. Furthermore, the concurrence between imbalanced labels can make harder the learning from certain labels. These are what we call textitdifficult labels. In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed. Specific metrics to assess this trait in multilabel datasets, called textitSCUMBLE (textitScore of ConcUrrence among iMBalanced LabEls) and textitSCUMBLELbl, are presented along with REMEDIAL (textitREsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels), a new algorithm aimed to relax label concurrence. How to deal with this problem using the R mldr package is also outlined.