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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17455 |
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| _version_ | 1866914002338054144 |
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| author | Freire-Oliveira, H. Paiva, E. R. F. Gama, J. Khan, L. Cerri, R. |
| author_facet | Freire-Oliveira, H. Paiva, E. R. F. Gama, J. Khan, L. Cerri, R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Systematic Literature Review on Multi-label Data Stream Classification Freire-Oliveira, H. Paiva, E. R. F. Gama, J. Khan, L. Cerri, R. Machine Learning 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. |
| title | A Systematic Literature Review on Multi-label Data Stream Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.17455 |