Authors: Stanojević, Bogdana 
Stanojević, Milan
Affiliations: Computer Science 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: On modeling regression in full interval-valued fuzzy environment
Journal: Procedia Computer Science
Volume: 221
First page: 1337
Last page: 1342
Issue Date: 2023
ISSN: 1877-0509
DOI: 10.1016/j.procs.2023.08.123
Abstract: 
We apply the general extension-principle-based approach to make predictions based on a regression model in a full interval-valued fuzzy environment. We use triangular interval-valued fuzzy numbers that model the uncertainty of the observed inputs and outputs to derive the predicted outputs in full accordance with Zadeh's extension principle. On one side, we enhance the Monte Carlo based algorithm introduced in the literature for simulating the output predictions of a fuzzy regression model by reducing the universe of random selections still keeping the accuracy of the empirical results; and on the other side, we solve quadratic models to derive the left endpoints of the α-cut intervals of the exact results. We use one real-life problem from hydrology engineering with data recalled from the literature to carry out numerical experiments and illustrate our proposed methodology.
Keywords: extension principle | fuzzy regression | interval-valued fuzzy number | least-squares method | Monte Carlo simulation
Publisher: Elsevier
Project: This work was partly supported by the Serbian Ministry of Science, Technological Development and Innova- tion through Mathematical Institute of the Serbian Academy of Sciences and Arts, and Faculty of Organizational Sciences, University of Belgrade.

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