Deep neuro-cognitive co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes

Ding, W; Lin, CT; Cao, Z

Deep neuro-cognitive co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes

Ding, W

Lin, CT

Cao, Z

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2019, 49 (7), pp. 2744 - 2757
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Ding, W https://orcid.org/0000-0002-3180-7347	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2019, 49 (7), pp. 2744 - 2757	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135058
dc.description.abstract	© 2013 IEEE. Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2018.2834390	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Deep neuro-cognitive co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	49	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access	*
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2013 IEEE. Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.

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http://hdl.handle.net/10453/135058