Support vector fuzzy regression with fuzzy input-fuzzy output and fuzzy error

Document Type : Original Article

Authors
Azam Moghadam 1
Mohsen Arefi 2
1 Department of Statistics, Faculty of Mathematical Sciences and Statistics, University of Birjand, Birjand, Iran.
2 Department of Statistics, Univrsity of Birjand, Birjand Iran
10.22034/jirss.2026.2075601.1148
Abstract
In this paper, we investigate a new approach of fuzzy regression analysis based on support vectors when the available data and error variable are fuzzy quantities. In this approach, based on the concept of the distance between two parallel hyper planes, we obtain the marginal hyper planes and then, based on some constraints on the fuzzy data, we present an optimization problem to estimate the parameters of fuzzy regression model. The proposed method is investigated in two cases: with fuzzy fixed error and with fuzzy variable errors. To evaluate the proposed support vector fuzzy regression (SVFR) models, we present two indices of goodness of fit. Based on these indices, the presented SVFR models are compared with some other approaches on the numerical and simulated examples.
Keywords
Fuzzy regression
Fuzzy error
Goodness of fit
Support vector
Subjects
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Journal of the Iranian Statistical Society
Volume 24, Issue 2
December 2025
Pages 107-124

  • Receive Date 23 October 2025
  • Revise Date 30 January 2026
  • Accept Date 09 April 2026