Distance measures, weighted averages, OWA operators and Bonferroni means

Merigó, JM; Palacios-Marqués, D; Soto-Acosta, P

Distance measures, weighted averages, OWA operators and Bonferroni means

Merigó, JM

Palacios-Marqués, D Soto-Acosta, P

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Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2017, 50 pp. 356 - 366
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Merigó, JM https://orcid.org/0000-0002-4672-6961	en_US
dc.contributor.author	Palacios-Marqués, D	en_US
dc.contributor.author	Soto-Acosta, P	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Applied Soft Computing Journal, 2017, 50 pp. 356 - 366	en_US
dc.identifier.issn	1568-4946	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126390
dc.description.abstract	© 2016 Elsevier B.V. The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This paper presents the OWA weighted average distance operator. The main advantage of this new approach is that it unifies the weighted Hamming distance and the OWA distance in the same formulation and considering the degree of importance that each concept has in the analysis. This operator includes a wide range of particular cases from the minimum to the maximum distance. Some further generalizations are also developed with generalized and quasi-arithmetic means. The use of Bonferroni means under this framework is also studied. The paper ends with an application of the new approach in a group decision making problem with Dempster-Shafer belief structure regarding the selection of strategies.	en_US
dc.relation.ispartof	Applied Soft Computing Journal	en_US
dc.relation.isbasedon	10.1016/j.asoc.2016.11.024	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Distance measures, weighted averages, OWA operators and Bonferroni means	en_US
dc.type	Journal Article
utslib.citation.volume	50	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	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 Information, Systems and Modelling
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	50	en_US

Abstract:

© 2016 Elsevier B.V. The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This paper presents the OWA weighted average distance operator. The main advantage of this new approach is that it unifies the weighted Hamming distance and the OWA distance in the same formulation and considering the degree of importance that each concept has in the analysis. This operator includes a wide range of particular cases from the minimum to the maximum distance. Some further generalizations are also developed with generalized and quasi-arithmetic means. The use of Bonferroni means under this framework is also studied. The paper ends with an application of the new approach in a group decision making problem with Dempster-Shafer belief structure regarding the selection of strategies.

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http://hdl.handle.net/10453/126390