Possibilistic Granular Count: Derivation and Extension to Granular Sum
- DOI
- 10.2991/asum.k.210827.064How to use a DOI?
- Keywords
- Granular count, Granular sum, Possibility theory, Uncertain data
- Abstract
Counting data in presence of uncertainty leads to granular counts that can be represented in terms of possibility distributions. The formula of granular count is derived on the basis of two weak assumptions that can be applied in a wide variety of problems involving uncertain data. The formulation is further extended to introduce the granular sum of counts, by taking into account the interactivity of granular counts. Numerical results show the differences in terms of specificity between granular sum and a direct application of the extension principle to sum granular counts.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Corrado Mencar PY - 2021 DA - 2021/08/30 TI - Possibilistic Granular Count: Derivation and Extension to Granular Sum BT - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP) PB - Atlantis Press SP - 486 EP - 493 SN - 2589-6644 UR - https://doi.org/10.2991/asum.k.210827.064 DO - 10.2991/asum.k.210827.064 ID - Mencar2021 ER -