Induced OWA operators in linear regression

Flores-Sosa, M; Avilés-Ochoa, E; Merigó, JM

Induced OWA operators in linear regression

Flores-Sosa, M Avilés-Ochoa, E Merigó, JM

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Publisher:: IOS PRESS
Publication Type:: Conference Proceeding
Citation:: Journal of Intelligent and Fuzzy Systems, 2020, 38, (5), pp. 5509-5520
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Flores-Sosa, M
dc.contributor.author	Avilés-Ochoa, E
dc.contributor.author	Merigó, JM
dc.date.accessioned	2021-02-23T11:58:32Z
dc.date.available	2021-02-23T11:58:32Z
dc.date.issued	2020-01-01
dc.identifier.citation	Journal of Intelligent and Fuzzy Systems, 2020, 38, (5), pp. 5509-5520
dc.identifier.issn	1064-1246
dc.identifier.issn	1875-8967
dc.identifier.uri	http://hdl.handle.net/10453/146339
dc.description.abstract	© 2020 - IOS Press and the authors. All rights reserved. The induced ordered weighted average (IOWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This work presents a new application that uses the simple linear regression (LR) and the IOWA operator in the same formulation. We study some of its main properties and particular cases. The main advantage of the linear regression IOWA operator is that it unifies the IOWA operator with the linear regression in the same formulation considering the degree of optimism and pessimism of the decision maker. Thus, we can under- or over- estimate the regression according to complex attitudes that the decision may have in the analysis. The work ends analyzing the applicability of this new approach in a problem regarding exchange rate forecasting. The objective of the new approach is to analyze the information in a more complete way.
dc.language	en
dc.publisher	IOS PRESS
dc.relation.ispartof	Journal of Intelligent and Fuzzy Systems
dc.relation.isbasedon	10.3233/JIFS-179642
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Induced OWA operators in linear regression
dc.type	Conference Proceeding
utslib.citation.volume	38
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-23T11:58:31Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	38
utslib.citation.issue	5

Abstract:

© 2020 - IOS Press and the authors. All rights reserved. The induced ordered weighted average (IOWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This work presents a new application that uses the simple linear regression (LR) and the IOWA operator in the same formulation. We study some of its main properties and particular cases. The main advantage of the linear regression IOWA operator is that it unifies the IOWA operator with the linear regression in the same formulation considering the degree of optimism and pessimism of the decision maker. Thus, we can under- or over- estimate the regression according to complex attitudes that the decision may have in the analysis. The work ends analyzing the applicability of this new approach in a problem regarding exchange rate forecasting. The objective of the new approach is to analyze the information in a more complete way.

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http://hdl.handle.net/10453/146339