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A new basic probability assignment generation and combination method for conflict data fusion in the evidence theory.

Yongchuan Tang1, Yonghao Zhou2, Xiangxuan Ren3

  • 1School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China. tangyongchuan@nwpu.edu.cn.

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This study introduces a novel method for generating basic probability assignments (BPAs) to resolve fusion paradoxes in Dempster-Shafer evidence theory. The approach enhances information fusion accuracy by adjusting BPAs using cosine similarity and belief entropy.

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Area of Science:

  • Artificial Intelligence
  • Information Fusion
  • Decision Theory

Background:

  • Dempster-Shafer evidence theory is widely used for information fusion.
  • The Dempster's combination rule faces challenges with fusion paradoxes.
  • Existing methods for generating basic probability assignments (BPAs) require improvement to handle these paradoxes.

Purpose of the Study:

  • To propose a new method for generating BPAs to address fusion paradoxes in Dempster-Shafer theory.
  • To improve the reliability and accuracy of information fusion.
  • To enhance the performance of classification tasks using the improved fusion method.

Main Methods:

  • A novel BPA generation method integrating cosine similarity and belief entropy.
  • Utilizing Mahalanobis distance to measure similarity between test samples and focal elements.
  • Adjusting BPAs based on reliability (cosine similarity) and uncertainty (belief entropy).
  • Applying Dempster's combination rule for fusing the adjusted BPAs.

Main Results:

  • The proposed method effectively resolves classical fusion paradoxes.
  • Numerical examples demonstrate the method's effectiveness in overcoming paradoxes.
  • Classification experiments show improved accuracy rates, verifying the method's rationality and efficiency.

Conclusions:

  • The new BPA generation method successfully tackles fusion paradoxes in Dempster-Shafer theory.
  • The approach enhances information fusion by providing more reliable and accurate BPAs.
  • This method offers a robust solution for classification problems and other information fusion applications.