# A Systematic Literature Review on Multi-label Data Stream Classification

arXiv: https://arxiv.org/abs/2508.17455 · HTML: https://mlsanity.com/paper/2508.17455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 140 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [A Unified View of Multi-Label Performance Measures](https://mlsanity.com/paper/1609.00288/) (2016)
- [A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams](https://mlsanity.com/paper/2106.09170/) (2021)
- [A Novel Online Stacked Ensemble for Multi-Label Stream Classification](https://mlsanity.com/paper/1809.09994/) (2018)
- [Online Metric Learning for Multi-Label Classification](https://mlsanity.com/paper/2006.07092/) (2020)
- [Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification](https://mlsanity.com/paper/2310.00665/) (2023)
- [A novel online multi-label classifier for high-speed streaming data applications](https://mlsanity.com/paper/1609.00086/) (2016)
- [Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models](https://mlsanity.com/paper/1704.05271/) (2017)
- [A Broad Ensemble Learning System for Drifting Stream Classification](https://mlsanity.com/paper/2110.03540/) (2021)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [An Online Universal Classifier for Binary, Multi-class and Multi-label Classification](https://mlsanity.com/paper/1609.00843/) (2016)
- [Evolving Multi-Label Fuzzy Classifier](https://mlsanity.com/paper/2203.15318/) (2022)
- [A Novel Progressive Multi-label Classifier for Classincremental Data](https://mlsanity.com/paper/1609.07215/) (2016)
- [Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification](https://mlsanity.com/paper/1711.05060/) (2017)

## Built on by (most cited first, 1 of 1)

- [Delay Complexity Dimension: Learning with Delayed and Censored Labels](https://mlsanity.com/paper/2610.18935/) (2026)

## Abstract (arXiv)

Classification in the context of multi-label data streams represents a challenge that has attracted significant attention due to its high real-world applicability. However, this task faces problems inherent to dynamic environments, such as the continuous arrival of data at high speed and volume, changes in the data distribution (concept drift), the emergence of new labels (concept evolution), and the latency in the arrival of ground truth labels. This systematic literature review presents an in-depth analysis of multi-label data stream classification proposals. We characterize the latest methods in the literature, providing a comprehensive overview, building a thorough hierarchy, and discussing how the proposals approach each problem. Furthermore, we discuss the adopted evaluation strategies and analyze the methods' asymptotic complexity and resource consumption. Finally, we identify the main gaps and offer recommendations for future research directions in the field.