# EvoSplit: An evolutionary approach to split a multi-label data set into disjoint subsets

arXiv: https://arxiv.org/abs/2102.06154 · HTML: https://mlsanity.com/paper/2102.06154/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [A Network Perspective on Stratification of Multi-Label Data](https://mlsanity.com/paper/1704.08756/) (2017)
- [Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning](https://mlsanity.com/paper/1901.01703/) (2019)
- [Synthetic Oversampling of Multi-Label Data based on Local Label Distribution](https://mlsanity.com/paper/1905.00609/) (2019)
- [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)

## Built on by (most cited first, 3 of 3)

- [Novel split quality measures for stratified multilabel Cross Validation with application to large and sparse gene ontology datasets](https://mlsanity.com/paper/2109.01425/) (2021)
- [Stratify or Die: Rethinking Data Splits in Image Segmentation](https://mlsanity.com/paper/2509.21056/) (2025)
- [Not All Nodes Are Created Equal: Homophily-Aware Stratification for Stable GNN Evaluation](https://mlsanity.com/paper/2609.19210/) (2026)

## Abstract (arXiv)

This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.