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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Multi-Criteria Decision-Making with Linguistic Labels

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DOI: http://dx.doi.org/10.15439/2022F218

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 263–267 ()

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Abstract. This paper proposes an approach that is suitable for solving multi-criteria decision-making problems that are characterized by fuzzy (subjective) criteria.A finite set (universe) of alternatives will be expressed as a decision table that represents a fuzzy information system, in which every fuzzy criterion is connected with a set of its linguistic values. We apply subjective preference degrees for linguistic values that should be provided by a decision-maker. To simplify the process of decision-making in big data environments, an additional stage will be introduced that can produce a smaller set of alternatives represented by fuzzy linguistic labels of similarity classes. We select a small set of similarity classes for a final ranking. A measure of compatibility will be defined that should express the accordance of a selected alternative with preferences given for the linguistic values of a particular fuzzy criterion.

References

  1. S. Greco, M. Ehrgott, and J. R. Figueira, Multiple Criteria Decision Analysis: State of the Art Surveys. New York: Springer-Verlag, 2016.
  2. A. M. Radzikowska and E. E. Kerre, “A comparative study of fuzzy rough sets,” Fuzzy Sets and Systems, vol. 126, pp. 137–155, 2002.
  3. L. D’eer and C. Cornelis, “A comprehensive study of fuzzy covering-based rough set models: Definitions, properties and interrelationships,” Fuzzy Sets and Systems, vol. 336, pp. 1–26, 2018.
  4. F. Cabrerizo, W. Pedrycz, I. Perez, S. Alonso, and E. Herrera-Viedma, “Group decision making in linguistic contexts: An information granulation approach,” Procedia Computer Science, vol. 91, pp. 715–724, 2016.
  5. S.-J. Chuu, “Interactive group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a supply chain,” European Journal of Operational Research, vol. 213, no. 1, pp. 279–289, 2011.
  6. W. Pedrycz, P. Ekel, and R. Parreiras, Fuzzy Multicriteria Decision-Making: Models, Methods and Applications. Chichester: John Wiley & Sons Ltd, 2011.
  7. C. Kahraman, S. C. Onar, and B. Oztaysi, “Fuzzy multicriteria decision-making: A literature review,” International Journal of Computational Intelligence Systems, vol. 8, no. 4, pp. 637–666, 2015.
  8. A. Mieszkowicz-Rolka and L. Rolka, “Labeled fuzzy rough sets versus fuzzy flow graphs,” in Proceedings of the 8th International Joint Conference on Computational Intelligence – Volume 2: FCTA, J. J. Merelo et al., Eds. SCITEPRESS Digital Library, 2016, pp. 115–120.
  9. ——, “A novel approach to fuzzy rough set-based analysis of information systems,” in Information Systems Architecture and Technology. Knowledge Based Approach to the Design, Control and Decision Support, ser. Advances in Intelligent Systems and Computing, Z. Wilimowska et al., Eds., vol. 432. Switzerland: Springer International Publishing, 2016, pp. 173–183.
  10. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data. Boston Dordrecht London: Kluwer Academic Publishers, 1991.