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Related Concept Videos

Decision Making: P-value Method01:09

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Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model.

Ayesha Sultan1, Wojciech Sałabun2,3, Shahzad Faizi4

  • 1Department of Statistics, Lahore Campus, COMSATS University Islamabad, Islamabad 45550, Pakistan.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary

This study introduces a probabilistic hesitant fuzzy linear regression model (PHFLRM) to improve decision-making by retaining more data. The new model addresses limitations in previous fuzzy regression techniques for multi-criteria decision-making problems.

Keywords:
FLRMMCDMPHFLRMPHFSpeters model

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Area of Science:

  • Decision Sciences
  • Fuzzy Set Theory
  • Regression Analysis

Background:

  • Hesitant Fuzzy Sets (HFS) are popular for decision-making but suffer from data loss.
  • Probabilistic Hesitant Fuzzy Sets (PHFS) were developed to improve HFS by incorporating probabilities and retaining more information.
  • Existing fuzzy regression models, like FLRM and hesitant fuzzy linear regression, lack distribution information crucial for complex decision-making.

Purpose of the Study:

  • To propose a novel Probabilistic Hesitant Fuzzy Linear Regression Model (PHFLRM) that incorporates distribution information.
  • To address limitations in existing models for multi-criteria decision-making (MCDM) problems.
  • To enhance data retention and decision accuracy in fuzzy environments.

Main Methods:

  • The PHFLRM treats input-output variables as probabilistic hesitant fuzzy elements (PHFEs).
  • A linear programming model (LPM) is utilized to estimate the parameters of the PHFLRM.
  • A case study is presented to demonstrate the model's application and effectiveness.
  • The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for comparison.
  • Spearman's rank correlation test validates the statistical significance of the results.

Main Results:

  • The proposed PHFLRM effectively incorporates distribution information, overcoming limitations of prior fuzzy regression models.
  • The case study demonstrates the practical applicability of the PHFLRM in MCDM scenarios.
  • Comparison with TOPSIS shows the robustness and potential advantages of the PHFLRM approach.
  • Statistical analysis confirms the significance of the obtained rankings.

Conclusions:

  • The PHFLRM offers a significant advancement in fuzzy decision-making by preserving more data and including distribution insights.
  • This model provides a more comprehensive approach to MCDM problems compared to traditional fuzzy regression methods.
  • The PHFLRM holds promise for improving the accuracy and reliability of expert evaluations in complex decision scenarios.