Basic Belief Assignment Determination Based on Radial Basis Function Network

Wei Li; Deqiang Han; Jean Dezert; Yi Yang

doi:10.62762/CJIF.2024.841250

Article Information

Published in Chinese Journal of Information Fusion

Volume/Issue Volume 1, Issue 3, 2024

Pages 175-182

Cited by 3 (Crossref) 2 (Scopus)

Abstract

In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue before the evidence fusion. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the compound-class samples with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the singleton and compound ones).Experimental results of evidence decision-based pattern classification on multiple UCI and image datasets show that our proposed method is rational and effective.

Graphical Abstract

Basic Belief Assignment Determination Based on Radial Basis Function Network

Keywords

Dempster-Shafer evidence theory evidence fusion basic belief assignment uncertainty modeling radial basis function network pattern classification

Data Availability Statement

Data will be made available on request.

Funding

This work was supported by National Natural Science Foundation of China under Grant 62473304 and Grant U22A2045.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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Cited By (3)

Xinghua Zhou, Luyuan Chen, Enze Mao, Yutong He. ODE-BPA: A Novel Basic Probability Assignment Generation Method Based on OTSU and Deng Entropy. Entropy, 2026 , 28 (6).
[CrossRef]
Luyuan Chen, Xinghua Zhou, Peidong Gao, Zhan Deng, Yu Yang, Yin Wu, Pierpaolo D’Urso. DDHRPS: A Data-Driven Hierarchical Method for Constructing Random Permutation Set From the Perspective of Layer-2 Belief Structure. IEEE Transactions on Fuzzy Systems, 2026 , 34 (4).
[CrossRef]
Qianli Zhou, Li Zhu, Yong Deng. Measure-based uncertainty with Dempster-Shafer structure. Science China Information Sciences, 2025 , 68 (10).
[CrossRef]

* Citation data provided by Crossref Cited-by.

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APA Style

Li, W., Han, D., Dezert, J., & Yang, Y. (2024). Basic Belief Assignment Determination Based on Radial Basis Function Network. Chinese Journal of Information Fusion, 1(3), 175–182. https://doi.org/10.62762/CJIF.2024.841250

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TY  - JOUR
AU  - Li, Wei
AU  - Han, Deqiang
AU  - Dezert, Jean
AU  - Yang, Yi
PY  - 2024
DA  - 2024/12/07
TI  - Basic Belief Assignment Determination Based on Radial Basis Function Network
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 1
IS  - 3
SP  - 175
EP  - 182
DO  - 10.62762/CJIF.2024.841250
UR  - https://www.icck.org/article/abs/CJIF.2024.841250
KW  - Dempster-Shafer evidence theory
KW  - evidence fusion
KW  - basic belief assignment
KW  - uncertainty modeling
KW  - radial basis function network
KW  - pattern classification
AB  - In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue before the evidence fusion. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the compound-class samples with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the singleton and compound ones).Experimental results of evidence decision-based pattern classification on multiple UCI and image datasets show that our proposed method is rational and effective.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Li2024Basic,
  author = {Wei Li and Deqiang Han and Jean Dezert and Yi Yang},
  title = {Basic Belief Assignment Determination Based on Radial Basis Function Network},
  journal = {Chinese Journal of Information Fusion},
  year = {2024},
  volume = {1},
  number = {3},
  pages = {175-182},
  doi = {10.62762/CJIF.2024.841250},
  url = {https://www.icck.org/article/abs/CJIF.2024.841250},
  abstract = {In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue before the evidence fusion. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the compound-class samples with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the singleton and compound ones).Experimental results of evidence decision-based pattern classification on multiple UCI and image datasets show that our proposed method is rational and effective.},
  keywords = {Dempster-Shafer evidence theory, evidence fusion, basic belief assignment, uncertainty modeling, radial basis function network, pattern classification},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}

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