The Emerging Trends of Multi-Label Learning.

Liu, W; Wang, H; Shen, X; Tsang, IW

The Emerging Trends of Multi-Label Learning.

Liu, W Wang, H Shen, X Tsang, IW

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44, (11), pp. 7955-7974
Issue Date:: 2022-11

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Field	Value	Language
dc.contributor.author	Liu, W
dc.contributor.author	Wang, H
dc.contributor.author	Shen, X
dc.contributor.author	Tsang, IW
dc.date.accessioned	2022-10-23T23:50:30Z
dc.date.available	2022-10-23T23:50:30Z
dc.date.issued	2022-11
dc.identifier.citation	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44, (11), pp. 7955-7974
dc.identifier.issn	0162-8828
dc.identifier.issn	1939-3539
dc.identifier.uri	http://hdl.handle.net/10453/162608
dc.description.abstract	Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there have been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfil this mission and delineate future research directions and new applications.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	http://purl.org/au-research/grants/arc/DP180100106
dc.relation	http://purl.org/au-research/grants/arc/DP200101328
dc.relation.ispartof	IEEE Transactions on Pattern Analysis and Machine Intelligence
dc.relation.isbasedon	10.1109/TPAMI.2021.3119334
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Big Data
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Big Data
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Big Data
dc.title	The Emerging Trends of Multi-Label Learning.
dc.type	Journal Article
utslib.citation.volume	44
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-10-23T23:50:29Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	44
utslib.citation.issue	11

Abstract:

Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there have been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfil this mission and delineate future research directions and new applications.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/162608