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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2019

06.09.2017 | Original Article

Prioritized induced probabilistic operator and its application in group decision making

verfasst von: Luis Alessandri Pérez-Arellano, Ernesto León-Castro, Ezequiel Avilés-Ochoa, José M. Merigó

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2019

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Abstract

A new extension of the ordered weighted average (OWA) operator is presented. This new operator includes the characteristics of three other operators: the prioritized, induced and probabilistic. The name is the prioritized induced probabilistic ordered weighted average (PIPOWA) operator. This operator can be used in a group decision-making process for selection of an alternative, taking into account three aspects: (1) not all of the decision-makers are equally important, (2) the probability of success of each alternative, and (3) an induced weighted vector. In the paper, some families of this operator are presented such as the prioritized probabilistic weighted average (PPOWA) operator and the prioritized induced ordered weighted average (PIOWA) operator. Additionally, some of the parameterized family of the aggregation operators, such as the minimum, maximum and total operator, are presented as special cases. The article also generalizes the PIPOWA operator by using quasi-arithmetic means. Finally, an example for selecting an alternative dispute resolution method in a commercial dispute is presented.
Vorheriger Artikel A discriminant binarization transform using genetic algorithm and error-correcting output code for face template protection
Nächster Artikel Facial semantic representation for ethnical Chinese minorities based on geometric similarity

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Metadaten
Titel
Prioritized induced probabilistic operator and its application in group decision making
verfasst von
Luis Alessandri Pérez-Arellano
Ernesto León-Castro
Ezequiel Avilés-Ochoa
José M. Merigó
Publikationsdatum
06.09.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0724-2

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