Introducing a trapezoidal interval type-2 fuzzy regression model

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dc.authoridTofigh Allahviranloo / 0000-0002-6673-3560en_US
dc.authorscopusidTofigh Allahviranloo / 8834494700en_US
dc.authorwosidTofigh Allahviranloo / V-4843-2019
dc.contributor.authorMokhtari, M.
dc.contributor.authorAllahviranloo, Tofigh
dc.contributor.authorBehzadi, M.H.
dc.contributor.authorLotfi, F.H.
dc.date.accessioned2022-02-23T13:07:00Z
dc.date.available2022-02-23T13:07:00Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractThe uncertainty is an important attribute about data that can arise from different sources including randomness and fuzziness, therefore in uncertain environments, especially, in modeling, planning, decision-making, and control under uncertainty, most data available contain some degree of fuzziness, randomness, or both, and at the same time, some of this data may be anomalous (outliers). In this regard, the new fuzzy regression approaches by creating a functional relationship between response and explanatory variables can provide efficient tools to explanation, prediction and possibly control of randomness, fuzziness, and outliers in the data obtained from uncertain environments. In the present study, we propose a new two-stage fuzzy linear regression model based on a new interval type-2 (IT2) fuzzy least absolute deviation (FLAD) method so that regression coefficients and dependent variables are trapezoidal IT2 fuzzy numbers and independent variables are crisp. In the first stage, to estimate the IT2 fuzzy regression coefficients and provide an initial model (by original dataset), we introduce two new distance measures for comparison of IT2 fuzzy numbers and propose a novel framework for solving fuzzy mathematical programming problems. In the second stage, we introduce a new procedure to determine the mild and extreme fuzzy outlier cutoffs and apply them to remove the outliers, and then provide the final model based on a clean dataset. Furthermore, to evaluate the performance of the proposed methodology, we introduce and employ suitable goodness of fit indices. Finally, to illustrate the theoretical results of the proposed method and explain how it can be used to derive the regression model with IT2 trapezoidal fuzzy data, as well as compare the performance of the proposed model with some well-known models using training data designed by Tanaka et al. [55], we provide two numerical examples. © 2022-IOS Press. All rights reserved.en_US
dc.identifier.citationMokhtari, M., Allahviranloo, T., Behzadi, M. H., & Lotfi, F. H. (2022). Introducing a trapezoidal interval type-2 fuzzy regression model. Journal of Intelligent and Fuzzy Systems, 42(3), 1381-1403. doi:10.3233/JIFS-210340en_US
dc.identifier.doi10.3233/JIFS-210340en_US
dc.identifier.endpage1403en_US
dc.identifier.issn1064-1246en_US
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85124647618en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1381en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-210340
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2499
dc.identifier.volume42en_US
dc.identifier.wosWOS:000752849700009en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAllahviranloo, Tofigh
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.relation.ispartofJournal of Intelligent and Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzinessen_US
dc.subjectIT2 Fuzzy Goal Programming Problemsen_US
dc.subjectIT2 Fuzzy Regression Parametersen_US
dc.subjectOutlier Cutoffsen_US
dc.subjectRandomnessen_US
dc.titleIntroducing a trapezoidal interval type-2 fuzzy regression modelen_US
dc.typeArticleen_US

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