Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix

Autores
Favret, Fabián; Labat, Marianela Daiana; Rodriguez, Federico Matias
Año de publicación
2014
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM).
Eje: XV Workshop de Agentes y Sistemas Inteligentes
Red de Universidades con Carreras de Informática (RedUNCI)
Materia
Ciencias Informáticas
AHP
multicriteria decision marking system
SVM
BPN
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/42404

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network_name_str SEDICI (UNLP)
spelling Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process MatrixFavret, FabiánLabat, Marianela DaianaRodriguez, Federico MatiasCiencias InformáticasAHPmulticriteria decision marking systemSVMBPNThe Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM).Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI)2014-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/42404spainfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:34:09Zoai:sedici.unlp.edu.ar:10915/42404Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:34:09.638SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
title Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
spellingShingle Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
Favret, Fabián
Ciencias Informáticas
AHP
multicriteria decision marking system
SVM
BPN
title_short Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
title_full Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
title_fullStr Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
title_full_unstemmed Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
title_sort Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
dc.creator.none.fl_str_mv Favret, Fabián
Labat, Marianela Daiana
Rodriguez, Federico Matias
author Favret, Fabián
author_facet Favret, Fabián
Labat, Marianela Daiana
Rodriguez, Federico Matias
author_role author
author2 Labat, Marianela Daiana
Rodriguez, Federico Matias
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
AHP
multicriteria decision marking system
SVM
BPN
topic Ciencias Informáticas
AHP
multicriteria decision marking system
SVM
BPN
dc.description.none.fl_txt_mv The Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM).
Eje: XV Workshop de Agentes y Sistemas Inteligentes
Red de Universidades con Carreras de Informática (RedUNCI)
description The Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM).
publishDate 2014
dc.date.none.fl_str_mv 2014-10
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info:eu-repo/semantics/publishedVersion
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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