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Multi-Label Ranking: Mining Multi-Label and Label Ranking Data

In: Machine Learning for Data Science Handbook

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  • Lihi Dery

    (Ariel University)

Abstract

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.

Suggested Citation

  • Lihi Dery, 2023. "Multi-Label Ranking: Mining Multi-Label and Label Ranking Data," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 511-535, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_23
    DOI: 10.1007/978-3-031-24628-9_23
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