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A Decision Probability Transformation Method Based on the Neural Network.

Junwei Li1, Aoxiang Zhao1, Huanyu Liu1

  • 1School of Artificial Intelligence, Henan University, Zhengzhou 450046, China.

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Summary
This summary is machine-generated.

This study introduces a neural network method to efficiently transform basic probability assignments (BPAs) into probabilities for improved decision-making in information fusion. The approach enhances applicability and reduces uncertainty in Dempster-Shafer evidence theory applications.

Keywords:
Dempster–Shafer evidence theoryaverage information contentinterval information contentneural networkprobabilistic information content

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Area of Science:

  • Artificial Intelligence
  • Information Science
  • Decision Science

Background:

  • The Dempster-Shafer evidence theory is crucial for information fusion.
  • Transforming basic probability assignments (BPAs) into probabilities is a key challenge for decision-making efficiency.
  • Existing methods face limitations in accuracy and applicability.

Purpose of the Study:

  • To propose an efficient neural network-based method for transforming BPAs to probabilistic decisions.
  • To enhance decision-making efficiency in information fusion using Dempster-Shafer theory.
  • To address the challenge of reasonable BPA to probability transformation.

Main Methods:

  • A neural network is constructed based on the BPA of propositions within a mass function.
  • Information content (average and interval) quantifies proposition subsets to create a weighting function with parameter *r*.
  • BPAs are allocated to hidden layers based on weights, outputting probabilities for single-element propositions.

Main Results:

  • The proposed method achieves consistent upper and lower probability boundaries for propositions.
  • It demonstrates higher applicability and lower uncertainty compared to existing methods.
  • Extensive examples and a practical application validate the method's effectiveness.

Conclusions:

  • The neural network-based probability transformation method offers a significant improvement for Dempster-Shafer evidence theory applications.
  • It provides a more reliable and efficient approach to information fusion and decision-making.
  • The method's ability to minimize uncertainty makes it valuable for complex data analysis.