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Robust Support Vector Data Description with Truncated Loss Function for Outliers Depression.

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A Decision-Making Model with Cloud Model, Z-Numbers, and Interval-Valued Linguistic Neutrosophic Sets.

Huakun Chen1,2, Jingping Shi1,2, Yongxi Lyu1,2

  • 1School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

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Summary

This study introduces a new Z-interval-valued linguistic neutrosophic set-trapezium-trapezium cloud (Z-IVLNS-TTC) model to better handle uncertainty. The novel approach improves information quantification and decision-making in complex scenarios.

Keywords:
Z-interval-valued linguistic neutrosophic set-trapezium–trapezium cloud (Z-IVLNS-TTC)Z-numbersgroup decision-makinginterval-valued linguistic neutrosophic sets (IVLNSs)trapezium cloud model

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Area of Science:

  • Decision Sciences
  • Information Science
  • Artificial Intelligence

Background:

  • Interval-valued linguistic neutrosophic sets (IVLNSs), Z-numbers, and trapezium clouds are key for modeling uncertainty.
  • Existing methods face challenges in accurately quantifying and processing complex uncertain information.

Purpose of the Study:

  • To develop a novel Z-interval-valued linguistic neutrosophic set-trapezium-trapezium cloud (Z-IVLNS-TTC) model.
  • To integrate IVLNSs and Z-numbers for enhanced expression of uncertainty.
  • To minimize information loss and distortion in quantification.

Main Methods:

  • A novel combination of IVLNSs and Z-numbers is introduced.
  • The Z-IVLNS-TTC model is proposed for improved information representation.
  • Objective weights are calculated using multi-objective programming (MOP).
  • A p-norm based distance measure for Z-IVLNS-TTCs is developed, inspired by TOPSIS.

Main Results:

  • The proposed Z-IVLNS-TTC model effectively reduces information loss and distortion.
  • A new objective weight calculation method using MOP is presented.
  • A novel distance measure enhances comparison of uncertain information.
  • The method demonstrates practical applicability in group decision-making.

Conclusions:

  • The Z-IVLNS-TTC model offers a robust framework for handling complex uncertainty.
  • The developed methods provide effective tools for decision-making under uncertainty.
  • Sensitivity analysis and comparisons confirm the method's effectiveness and feasibility.