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A new weighting factor in combining belief function.

Deyun Zhou1, Qian Pan1, Gyan Chhipi-Shrestha2

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Summary
This summary is machine-generated.

This study introduces a novel weighting factor to improve Dempster-Shafer evidence theory, effectively resolving conflicts in fused evidence for better real-world applications like water quality monitoring.

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Area of Science:

  • Artificial Intelligence
  • Information Fusion
  • Environmental Science

Background:

  • Dempster-Shafer evidence theory is widely applied but struggles with conflicting evidence, leading to counter-intuitive outcomes.
  • Existing methods like discounted and weighted average evidence have limitations in handling conflicting data.
  • Resolving counter-intuitive results from classical Dempster-Shafer combination rules remains a challenge.

Purpose of the Study:

  • To propose a new weighting factor for Dempster-Shafer evidence theory to address issues with conflicting evidence fusion.
  • To develop a robust method for combining conflicting evidence that overcomes limitations of existing approaches.
  • To enhance the accuracy and reliability of evidence fusion in complex systems.

Main Methods:

  • A modified dissimilarity measurement considering both distance and conflict between evidences.
  • Introduction of Deng entropy to quantify the information volume of each piece of evidence.
  • Combination of two derived weights to create a novel weighting factor for a weighted average method.

Main Results:

  • The proposed method demonstrates validity and effectiveness through numerical examples.
  • The new weighting factor successfully addresses counter-intuitive outcomes in conflicting evidence fusion.
  • The approach shows significant improvements over existing methods for handling evidence conflicts.

Conclusions:

  • The developed weighting factor and method offer a significant advancement in Dempster-Shafer evidence theory.
  • The method provides a more reliable approach to fusing conflicting evidence, enhancing decision-making.
  • Successful application in river water quality monitoring highlights its practical utility in identifying pollution sources.