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A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and

Qi Tang1, Jianyu Xiao1, Kefeng Wu1

  • 1School of Computer Science and Technology, Huaibei Normal University, HuaiBei, AnHui, China.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

This study introduces a novel evidence combination method to address limitations in the Dempster-Shafer (D-S) rule when dealing with conflicting evidence. The approach enhances D-S rule performance by integrating the SALSA algorithm and Lance-Williams distance for more robust data fusion.

Keywords:
Correlation coefficientD-S combination ruleLance-Williams distanceSALSA algorithm

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Area of Science:

  • Artificial Intelligence
  • Data Fusion
  • Information Theory

Background:

  • The Dempster-Shafer (D-S) combination rule struggles with highly conflicting evidence.
  • Existing methods lack effective strategies for managing contradictory data inputs.

Purpose of the Study:

  • To propose a new evidence combination method that overcomes the limitations of the traditional D-S rule with conflicting evidence.
  • To enhance the accuracy and reliability of data fusion in the presence of high conflict.

Main Methods:

  • Developed a method combining the Stochastic Approach for Link-structure Analysis (SALSA) algorithm with Lance-Williams distance.
  • Calculated evidence conflict using correlation coefficients and constructed an evidence association network.
  • Derived authority weights using SALSA and evidence support, then transformed Lance-Williams distance into support.
  • Created a novel correction coefficient using authority weight and evidence support to adjust conflicting evidence.
  • Fused corrected evidence using the D-S combination rule.

Main Results:

  • The proposed method effectively handles highly conflicting evidence, a known issue with the traditional D-S rule.
  • Numerical results demonstrate the improved performance and robustness of the new evidence combination technique.
  • The integration of SALSA and Lance-Williams distance provides a significant improvement in evidence fusion accuracy.

Conclusions:

  • The novel evidence combination method successfully addresses the limitations of the traditional D-S rule in handling conflicting evidence.
  • The proposed approach offers a more reliable solution for data fusion tasks involving contradictory information.
  • This research contributes a valuable technique for improving the accuracy of evidence combination in complex scenarios.