Mathematical programming approach to formulate intuitionistic fuzzy regression model based on least absolute deviations

Liang Hsuan Chen, Sheng Hsing Nien

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Fuzzy regression models are widely used to investigate the relationship between explanatory and response variables for many decision-making applications in fuzzy environments. To include more fuzzy information in observations, this study uses intuitionistic fuzzy numbers (IFNs) to characterize the explanatory and response variables in formulating intuitionistic fuzzy regression (IFR) models. Different from traditional solution methods, such as the least-squares method, in this study, mathematical programming problems are built up based on the criterion of least absolute deviations to establish IFR models with intuitionistic fuzzy parameters. The proposed approach has the advantages that the model formulation is not limited to the use of symmetric triangular IFNs and the signs of the parameters are determined simultaneously in the model formulation process. The prediction performance of the obtained models is evaluated in terms of similarity and distance measures. Comparison results of the performance measures indicate that the proposed models outperform an existing approach.

Original languageEnglish
Pages (from-to)191-210
Number of pages20
JournalFuzzy Optimization and Decision Making
Volume19
Issue number2
DOIs
Publication statusPublished - 2020 Jun 1

All Science Journal Classification (ASJC) codes

  • Software
  • Logic
  • Artificial Intelligence

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