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

Lihi Dery ()
Additional contact information
Lihi Dery: Ariel University

A chapter in Machine Learning for Data Science Handbook, 2023, pp 511-535 from Springer

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_23

Ordering information: This item can be ordered from
http://www.springer.com/9783031246289

DOI: 10.1007/978-3-031-24628-9_23

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().