Paternain Dallo, Daniel

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https://academica-e.unavarra.es/handle/2454/49749

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Paternain Dallo

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Daniel

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Estadística, Informática y Matemáticas

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ISC. Institute of Smart Cities

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0000-0002-5845-887X

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88758

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810056

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2019 - 20202
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Author: Jin, LeSheng × Subject: Aggregation function ×

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Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Some preference involved aggregation models for basic uncertain information using uncertainty transformation
    (IOS Press, 2020) Yang, RouJian; Jin, LeSheng; Paternain Dallo, Daniel; Yager, Ronald R.; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    In decision making, very often the data collected are with different extents of uncertainty. The recently introduced concept, Basic Uncertain Information (BUI), serves as one ideal information representation to well model involved uncertainties with different extents. This study discusses some methods of BUI aggregation by proposing some uncertainty transformations for them. Based on some previously obtained results, we at first define Iowa operator with poset valued input vector and inducing vector. The work then defines the concept of uncertain system, on which we can further introduce the multi-layer uncertainty transformation for BUI. Subsequently, we formally introduce MUT-Iowa aggregation procedure, which has good potential to more and wider application areas. A numerical example is also offered along with some simple usage of it in decision making.
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    PublicationOpen Access
    Nested formulation paradigms for induced ordered weighted averaging aggregation for decision‐making and evaluation
    (Wiley, 2019) Zhu, Chen; Jin, LeSheng; Mesiar, Radko; Yager, Ronald R.; Paternain Dallo, Daniel; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Existing extensions to Yager's ordered weighted aver-aging (OWA) operators enlarge the application rangeand to encompass more principles and properties relatedto OWA aggregation. However, these extensions do notprovide a strict and convenient way to model evaluationscenarios with complex or grouped preferences. Basedon earlier studies and recent evolutionary changes inOWA operators, we propose formulation paradigms forinduced OWA aggregation and a related weight functionwith self‐contained properties that make it possibleto model such complex preference‐involved evaluationproblems in a systematic way. The new formulationshave some recursive forms that provide more waysto apply OWA aggregation and deserve further studyfrom a mathematical perspective. In addition, the newproposal generalizes almost all of the well‐knownextensions to the original OWA operators. We providean example showing the representative use of suchparadigms in decision‐making and evaluation problems.