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

Lihi Dery

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

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

Lihi Dery

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Publisher	Springer International Publishing
Pages	511-535
Number of pages	25
ISBN (Electronic)	9783031246289
ISBN (Print)	9783031246272
DOIs	https://doi.org/10.1007/978-3-031-24628-9_23
State	Published - 1 Jan 2023
Externally published	Yes

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

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

Cite this

@inbook{d5fa3353256241cab194b3d1e52418a8,

title = "Multi-Label Ranking: Mining Multi-Label and Label Ranking Data",

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.",

author = "Lihi Dery",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2023.",

year = "2023",

month = jan,

day = "1",

doi = "10.1007/978-3-031-24628-9\_23",

language = "English",

isbn = "9783031246272",

pages = "511--535",

booktitle = "Machine Learning for Data Science Handbook",

publisher = "Springer International Publishing",

}

TY - CHAP

T1 - Multi-Label Ranking

T2 - Mining Multi-Label and Label Ranking Data

AU - Dery, Lihi

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85195576014

U2 - 10.1007/978-3-031-24628-9_23

DO - 10.1007/978-3-031-24628-9_23

M3 - Chapter

AN - SCOPUS:85195576014

SN - 9783031246272

SP - 511

EP - 535

BT - Machine Learning for Data Science Handbook

PB - Springer International Publishing

ER -

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this