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Research on linguistic multi-attribute decision making method for normal cloud similarity.

Changlin Xu1,2, Li Yang1

  • 1School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.

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|October 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel normal cloud similarity method to address uncertainty in linguistic multi-attribute decision-making. The approach demonstrates superior similarity discrimination, classification accuracy, and efficiency compared to existing techniques.

Keywords:
Entropy-containing expectation curveMulti-attribute decision makingNormal cloud modelSimilarity measureTOPSIS methodWasserstein distance

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Area of Science:

  • Decision Sciences
  • Artificial Intelligence
  • Probability Theory

Background:

  • Linguistic multi-attribute decision-making (LMAD) faces challenges with uncertainty and linguistic transformation.
  • Existing methods struggle to effectively quantify and compare uncertain linguistic information.

Purpose of the Study:

  • To propose a novel decision-making method based on normal cloud similarity to handle linguistic uncertainty.
  • To develop a new similarity measure for normal clouds using Wasserstein distance.
  • To validate the method's performance in linguistic decision-making scenarios.

Main Methods:

  • Developed a normal cloud similarity measure utilizing Wasserstein distance and normal cloud characteristic curves.
  • Analyzed the properties of the proposed similarity measure.
  • Employed numerical simulations and time series classification experiments for performance evaluation.
  • Applied the method to linguistic multi-attribute decision-making using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach.

Main Results:

  • The proposed normal cloud similarity method exhibits strong discrimination ability for uncertain linguistic information.
  • Achieved high classification accuracy in time series data classification experiments.
  • Demonstrated a low computational cost (CPU time).
  • Successfully applied to linguistic multi-attribute decision-making for scheme ranking.

Conclusions:

  • The novel normal cloud similarity measure effectively addresses the uncertainty linguistic transformation problem in LMAD.
  • The method offers a robust and efficient alternative for complex decision-making problems involving linguistic data.
  • The integration with TOPSIS provides a practical framework for final scheme selection.