Preference Neural Network

Elgharabawy, A; Prasad, M; Lin, CT

Preference Neural Network

Elgharabawy, A Prasad, M

Lin, CT

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, PP, (99), pp. 1-15
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Elgharabawy, A
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Lin, CT
dc.date.accessioned	2023-07-25T03:59:09Z
dc.date.available	2023-07-25T03:59:09Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, PP, (99), pp. 1-15
dc.identifier.issn	2471-285X
dc.identifier.issn	2471-285X
dc.identifier.uri	http://hdl.handle.net/10453/171645
dc.description.abstract	This paper proposes a novel label ranker network to learn the relationship between labels to solve ranking and classification problems. The Preference Neural Network (<italic>PNN</italic>) uses <italic>spearman</italic> correlation gradient ascent and two new activation functions, positive smooth staircase (<italic>PSS</italic>), and smooth staircase (<italic>SS</italic>) that accelerate the ranking by creating almost deterministic preference values. <italic>PNN</italic> is proposed in two forms, fully connected simple Three layers and Preference Net (<italic>PN</italic>), where the latter is the deep ranking form of <italic>PNN</italic> to learning feature selection using ranking to solve images classification problem. <italic>PN</italic> uses a new type of ranker kernel to generate a feature map. <italic>PNN</italic> outperforms five previously proposed methods for label ranking, obtaining state-of-the-art results on label ranking, and <italic>PN</italic> achieves promising results on <italic>CFAR-100</italic> with high computational efficiency.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation.ispartof	IEEE Transactions on Emerging Topics in Computational Intelligence
dc.relation.isbasedon	10.1109/TETCI.2023.3268707
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.title	Preference Neural Network
dc.type	Journal Article
utslib.citation.volume	PP
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-07-25T03:59:07Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This paper proposes a novel label ranker network to learn the relationship between labels to solve ranking and classification problems. The Preference Neural Network (PNN) uses spearman correlation gradient ascent and two new activation functions, positive smooth staircase (PSS), and smooth staircase (SS) that accelerate the ranking by creating almost deterministic preference values. PNN is proposed in two forms, fully connected simple Three layers and Preference Net (PN), where the latter is the deep ranking form of PNN to learning feature selection using ranking to solve images classification problem. PN uses a new type of ranker kernel to generate a feature map. PNN outperforms five previously proposed methods for label ranking, obtaining state-of-the-art results on label ranking, and PN achieves promising results on CFAR-100 with high computational efficiency.

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http://hdl.handle.net/10453/171645