LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE-Reference-Cited by-同舟云学术

LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE

Published:2010-06 Issue:03 Volume:18 Page:287-304
ISSN:0218-4885
Container-title:International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
language:en
Short-container-title:Int. J. Unc. Fuzz. Knowl. Based Syst.

Author:

MERIGÓ J. M.¹,CASANOVAS M.¹,MARTÍNEZ L.²

Affiliation:

1. Department of Business Administration, University of Barcelona, Av. Diagonal 690 08034, Barcelona, Spain

2. Department of Computer Science, University of Jaén, Campus Las Lagunillas s/n Jaén, 23071, Spain

Abstract

In this paper, we develop a new approach for decision making with Dempster-Shafer theory of evidence by using linguistic information. We suggest the use of different types of linguistic aggregation operators in the model. We then obtain as a result, the belief structure — linguistic ordered weighted averaging (BS-LOWA), the BS — linguistic hybrid averaging (BS-LHA) and a wide range of particular cases. Some of their main properties are studied. Finally, we provide an illustrative example that shows the different results obtained by using different types of linguistic aggregation operators in the new approach.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218488510006544

Reference47 articles.

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