Covariance in Ordered Weighted Logarithm Aggregation Operators

Perez-Romero, ME; Alfaro-Garcia, VG; Merigo, JM; Flores-Romero, MB

Covariance in Ordered Weighted Logarithm Aggregation Operators

Perez-Romero, ME Alfaro-Garcia, VG Merigo, JM Flores-Romero, MB

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, 00, pp. 36-41
Issue Date:: 2021-01-05

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Field	Value	Language
dc.contributor.author	Perez-Romero, ME
dc.contributor.author	Alfaro-Garcia, VG
dc.contributor.author	Merigo, JM
dc.contributor.author	Flores-Romero, MB
dc.date	2020-12-01
dc.date.accessioned	2022-06-06T06:14:30Z
dc.date.available	2022-06-06T06:14:30Z
dc.date.issued	2021-01-05
dc.identifier.citation	2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, 00, pp. 36-41
dc.identifier.isbn	978-1-7281-2547-3
dc.identifier.uri	http://hdl.handle.net/10453/157976
dc.description.abstract	Covariance as a measurement of dispersion allows knowing the behavior of one variable based on another. Its characteristic mechanism makes it a basic component in decision-making processes. This paper introduces some covariance logarithmic aggregation operators including the covariance generalized weighted logarithmic aggregation (Cov-GWLA) operator, the generalized covariance ordered weighted logarithmic aggregation (Cov-GOWLA) operator, the covariance ordered weighted logarithmic aggregation (Cov-OWLA) operator, the induced generalized covariance ordered weighted logarithmic aggregation (Cov-IGOWLA) operator and the induced covariance ordered weighted logarithmic aggregation (Cov-IOWLA) operator. The introduced tools extend the available information when using logarithmic aggregation operators, thus aiding decision and policy makers in the analysis of complex phenomena.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.ispartof	IEEE Symposium Series on Computational Intelligence
dc.relation.isbasedon	10.1109/ssci47803.2020.9308163
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Covariance in Ordered Weighted Logarithm Aggregation Operators
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Canberra, ACT, Australia
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-06T06:14:29Z
pubs.finish-date	2020-12-04
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-12-01
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Covariance as a measurement of dispersion allows knowing the behavior of one variable based on another. Its characteristic mechanism makes it a basic component in decision-making processes. This paper introduces some covariance logarithmic aggregation operators including the covariance generalized weighted logarithmic aggregation (Cov-GWLA) operator, the generalized covariance ordered weighted logarithmic aggregation (Cov-GOWLA) operator, the covariance ordered weighted logarithmic aggregation (Cov-OWLA) operator, the induced generalized covariance ordered weighted logarithmic aggregation (Cov-IGOWLA) operator and the induced covariance ordered weighted logarithmic aggregation (Cov-IOWLA) operator. The introduced tools extend the available information when using logarithmic aggregation operators, thus aiding decision and policy makers in the analysis of complex phenomena.

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http://hdl.handle.net/10453/157976