Why Kappa Regression?
Authors
Julio C. Urenda, Orsolya Csiszár, Gábor Csiszár, József Dombi, György Eigner, Olga Kosheleva, Vladik Kreinovich
Corresponding Author
Julio C. Urenda
Available Online 30 August 2021.
- DOI
- 10.2991/asum.k.210827.063How to use a DOI?
- Keywords
- Kappa-regression distributions, Kappa-regression membership functions, Invariance
- Abstract
A recent book provides examples that a new class of probability distributions and membership functions – called kappa-regression distributions and membership functions – leads, in many practical applications, to better data processing results than using previously known classes. In this paper, we provide a theoretical explanation for this empirical success – namely, we show that these distributions are the only ones that satisfy reasonable invariance requirements.
- 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 - Julio C. Urenda AU - Orsolya Csiszár AU - Gábor Csiszár AU - József Dombi AU - György Eigner AU - Olga Kosheleva AU - Vladik Kreinovich PY - 2021 DA - 2021/08/30 TI - Why Kappa Regression? 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 - 478 EP - 485 SN - 2589-6644 UR - https://doi.org/10.2991/asum.k.210827.063 DO - 10.2991/asum.k.210827.063 ID - Urenda2021 ER -