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A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster-Shafer Evidence Theory for

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Summary

This study introduces a new reliability coefficient for Dempster-Shafer evidence theory to improve the fusion of conflicting evidence. The method enhances uncertain information processing and yields more intuitive results in evidence fusion.

Keywords:
Dempster–Shafer evidence theoryconflict data fusionevidence reliability coefficientuncertain information fusionuncertainty measure

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Area of Science:

  • Artificial Intelligence
  • Information Fusion
  • Uncertainty Quantification

Background:

  • Dempster-Shafer evidence theory is a key method for handling uncertain information.
  • The standard Dempster combination rule can produce counterintuitive results with conflicting evidence.
  • Resolving conflicts in evidence fusion remains an open research challenge.

Purpose of the Study:

  • To propose a novel reliability coefficient for Dempster-Shafer evidence theory.
  • To address the issue of conflicting evidence fusion.
  • To enhance the processing of uncertain and conflicting information.

Main Methods:

  • A new reliability coefficient is developed using betting commitment evidence distance.
  • A single belief function is defined for initial belief assignment.
  • Evidence is preprocessed using the proposed reliability coefficient and single belief function.
  • The Dempster combination rule is applied for evidence fusion post-preprocessing.

Main Results:

  • The proposed method effectively preprocesses conflicting and uncertain evidence.
  • Experimental results on UCI machine learning datasets demonstrate the method's effectiveness.
  • The new reliability coefficient improves the accuracy of uncertain information fusion.

Conclusions:

  • The novel reliability coefficient offers a viable solution for conflict handling in Dempster-Shafer evidence theory.
  • The proposed approach enhances the fusion of uncertain and conflicting information.
  • This work contributes to more robust and intuitive evidence fusion techniques.