Multi-label Ranking: Mining Multi-label and Label Ranking Data
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Abstract (arXiv)
We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 33 of its 169 references that are in the map. First posted on arXiv 3 Jan 2021.
Builds on
33 of 169 references.
References · 33
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21 more
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.