# A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research

arXiv: https://arxiv.org/abs/2512.08371 · HTML: https://mlsanity.com/paper/2512.08371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Clarabel: An interior-point solver for conic programs with quadratic objectives](https://mlsanity.com/paper/2405.12762/) (2024)
- [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)
- [Making Classifier Chains Resilient to Class Imbalance](https://mlsanity.com/paper/1807.11393/) (2018)
- [Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations](https://mlsanity.com/paper/2412.16521/) (2024)

## Abstract (arXiv)

Datasets may contain observations with multiple labels. If the labels are not mutually exclusive, and if the labels vary greatly in frequency, obtaining a sample that includes sufficient observations with scarcer labels to make inferences about those labels, and which deviates from the population frequencies in a known manner, creates challenges. In this paper, we consider a multivariate Bernoulli distribution as our underlying distribution of a multi-label problem. We present a novel sampling algorithm that takes label dependencies into account. It uses observed label frequencies to estimate multivariate Bernoulli distribution parameters and calculates weights for each label combination. This approach ensures the weighted sampling acquires target distribution characteristics while accounting for label dependencies. We applied this approach to a variety of datasets, including a sample of research articles from Web of Science labeled with 64 biomedical topic categories. We aimed to preserve category frequency order, reduce frequency differences between most and least common categories, and account for category dependencies. This approach produced a more balanced sub-sample, enhancing the representation of minority categories.