pith. sign in

arxiv: 2605.17038 · v1 · pith:IR246SA6new · submitted 2026-05-16 · 💻 cs.AI

Evidential Information Fusion on Possibilistic Structure

Qianli Zhou , Ye Cui , Zhen Li , Witold Pedrycz , Yong Deng This is my paper

Pith reviewed 2026-05-19 20:04 UTC · model grok-4.3

classification 💻 cs.AI
keywords evidential fusionbelief functionspossibilistic structuretriangular normsDempster's ruleinformation fusionisopignistic principleconflict management
0
0 comments X

The pith

A reversible transformation from belief functions to possibilistic structures on the power set enables a flexible evidential fusion framework using triangular norms.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to overcome the rigid intersection semantics of Dempster's rule, which restricts how belief functions from multiple sources can be combined. It does so by defining a reversible mapping, rooted in the isopignistic principle, that converts belief functions into a possibilistic structure defined directly on the power set of hypotheses. Within this structure, subset relationships are tracked explicitly through a belief evolution network rather than a conventional mass function. The authors then apply the family of triangular norms to define combination operations that adapt to different source conditions. A sympathetic reader would care because many practical fusion tasks involve non-distinct sources, internal conflicts, or mixed data types where the classical rule either fails or produces counterintuitive results.

Core claim

Dempster's rule imposes strong structural restrictions through its intersection-based semantics. The authors introduce a reversible transformation, derived from the isopignistic principle, between belief functions and a possibilistic structure defined on the power set. In this structure, relationships among subsets are characterized by a belief evolution network that supplies a more flexible representation than the standard mass function. Building on this representation, the triangular norm family is used to construct a general and adaptive evidential information fusion framework that supports more flexible combination behaviors.

What carries the argument

Reversible transformation from belief functions to possibilistic structure on the power set via the isopignistic principle, with subset relationships captured explicitly by a belief evolution network.

If this is right

  • The framework supports more flexible combination behaviors than methods rooted in Dempster semantics.
  • It exhibits advantages when fusing information from non-distinct sources.
  • It provides improved conflict management during combination.
  • It allows parametric design of the combination operation.
  • It extends naturally to heterogeneous information fusion tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The parametric flexibility of the triangular-norm layer could be used to encode source reliability estimates learned from data.
  • The same transformation might serve as a bridge between belief-function and possibility-theory toolkits in hybrid uncertainty systems.
  • Applications such as multi-sensor tracking could test whether the belief evolution network yields more stable outputs under changing source quality.

Load-bearing premise

The isopignistic principle supplies a valid reversible mapping from belief functions to the possibilistic structure on the power set that preserves the relationships needed for fusion.

What would settle it

A concrete counter-example in which the transformed structure combined via a chosen triangular norm yields less coherent results than Dempster's rule on a known set of conflicting belief functions would falsify the claimed advantages.

Figures

Figures reproduced from arXiv: 2605.17038 by Qianli Zhou, Witold Pedrycz, Ye Cui, Yong Deng, Zhen Li.

Figure 1
Figure 1. Figure 1: Mapping structural dependency among focal sets into BEN. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed method. It can be directly rewritten as the fused isopignistic relative function Ibm◦ . Specifically, Ibm◦ (∅) = em◦ , Ibm◦ ({ω}) = π (1)◦ ({ω}), and for each higher layer, Ibm◦ (F) = π (t)◦ (F), F ∈ L(t) , t = 2, . . . , n. Finally, the fused isopignistic relative function is reconstructed into the fused BPA: ϖ◦ −→ Ibm◦ −→ Im◦ −→ m◦ = m1 π · · · π mk. Therefore, the proposed frame… view at source ↗
Figure 3
Figure 3. Figure 3: Variations of fused masses when the parameters change. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of multi-view classification. TABLE VI MULTI-VIEW PROTOCOLS USED IN REAL-DATA EXPERIMENTS. Protocol Data Partition Overlap setting Wine-C1 Wine Contiguous Each view borrows one feature from each other view; sizes 7/6/6. D0–4-R6 Digits 0–4 Round-robin Each view borrows six pixels from each other view; sizes 34/33/33. D0–4-D4 Digits 0–4 Diagonal Each view borrows four pixels from each other view; si… view at source ↗
read the original abstract

Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in handling complex source states and diverse information fusion scenarios. To overcome this limitation, we propose a reversible transformation, derived from the isopignistic principle, between belief functions and a possibilistic structure defined on the power set. In this transformation, the relationships among subsets are explicitly characterized by a belief evolution network, which provides a more flexible representation of evidential information beyond the conventional mass function structure. On this basis, we further introduce the triangular norm family to develop a general and adaptive evidential information fusion framework. Unlike fusion methods rooted in Dempster semantics, the proposed framework supports more flexible combination behaviors and exhibits advantages in non-distinct source fusion, conflict management, parametric combination design, and heterogeneous information fusion.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a reversible transformation, derived from the isopignistic principle, that maps belief functions to a possibilistic structure defined on the power set. Relationships among subsets are represented explicitly via a belief evolution network, which then serves as the basis for applying a triangular-norm family to obtain a general, adaptive evidential fusion framework. The authors claim this yields more flexible combination behaviors than Dempster’s rule, with advantages for non-distinct sources, conflict management, parametric design, and heterogeneous information fusion.

Significance. If the mapping is shown to be bijective, to preserve the necessary relational structure, and to commute appropriately with the subsequent t-norm operator, the framework could meaningfully extend the representational power of belief-function theory beyond intersection-based semantics. The parametric flexibility introduced by t-norms would constitute a concrete, usable advance for applications requiring tunable conflict handling.

major comments (3)
  1. [§3.2] §3.2 (Isopignistic transformation): the reversibility claim is stated but no explicit inverse mapping or proof is supplied that recovers an arbitrary mass function from the possibilistic structure; without this, it is impossible to verify that the transform is bijective or that conflict mass is preserved.
  2. [§4.1] §4.1 (Belief evolution network): the network is introduced to characterize subset relationships, yet no demonstration is given that the network structure is independent of the chosen t-norm parameters or that fusion results commute with the forward and inverse transforms; this directly affects the asserted advantages in conflict management and non-distinct source fusion.
  3. [§5] §5 (Fusion experiments): the reported comparisons with Dempster’s rule and other baselines lack an ablation that isolates the contribution of the isopignistic mapping itself; without such controls it remains unclear whether observed improvements stem from the new representation or from the particular t-norm parameterization.
minor comments (2)
  1. [Eq. (7)] The definition of the belief evolution network (Eq. (7)) uses an informal adjacency notation; a small matrix or graph diagram would make the construction unambiguous.
  2. Several t-norm parameter settings are introduced without a clear statement of the admissible range or default values; a short table would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Isopignistic transformation): the reversibility claim is stated but no explicit inverse mapping or proof is supplied that recovers an arbitrary mass function from the possibilistic structure; without this, it is impossible to verify that the transform is bijective or that conflict mass is preserved.

    Authors: We agree that an explicit inverse mapping and formal proof are required to fully substantiate the reversibility claim and to allow verification of bijectivity and conflict-mass preservation. The current manuscript derives the forward transformation from the isopignistic principle but does not supply the inverse or the accompanying proof. In the revised version we will add a dedicated subsection to §3.2 that presents the inverse mapping explicitly and includes a proof establishing bijectivity together with preservation of conflict mass. revision: yes

  2. Referee: [§4.1] §4.1 (Belief evolution network): the network is introduced to characterize subset relationships, yet no demonstration is given that the network structure is independent of the chosen t-norm parameters or that fusion results commute with the forward and inverse transforms; this directly affects the asserted advantages in conflict management and non-distinct source fusion.

    Authors: The observation is correct: the manuscript introduces the belief evolution network to represent subset relationships but does not demonstrate its independence from t-norm parameters or the commutativity of fusion results with the forward and inverse transforms. We will revise §4.1 to include a formal analysis showing that the network structure remains invariant under the t-norm family and that the overall fusion operation commutes with the transformation pair, thereby supporting the claimed advantages for conflict management and non-distinct sources. revision: yes

  3. Referee: [§5] §5 (Fusion experiments): the reported comparisons with Dempster’s rule and other baselines lack an ablation that isolates the contribution of the isopignistic mapping itself; without such controls it remains unclear whether observed improvements stem from the new representation or from the particular t-norm parameterization.

    Authors: We recognize that the existing experimental comparisons do not isolate the contribution of the isopignistic mapping through ablation. In the revised manuscript we will augment §5 with additional ablation studies that systematically vary the presence of the isopignistic mapping while holding t-norm parameters fixed, thereby clarifying the specific role of the new representation in the reported performance gains. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation rests on external isopignistic principle and t-norm operators

full rationale

The abstract presents a reversible transformation derived from the isopignistic principle to a possibilistic structure on the power set, followed by application of the triangular norm family for fusion. No equations, parameter fits, or self-citations are shown that reduce the claimed advantages (flexible combination, conflict management) to the inputs by construction. The mapping is asserted as providing a belief evolution network that enables the new behaviors, but the provided text contains no self-definitional loop, fitted-input prediction, or load-bearing self-citation chain. The framework is therefore treated as self-contained against external benchmarks for the purpose of this circularity pass.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central proposal rests on the isopignistic principle as the basis for the transformation and on the assumption that triangular norms can be adapted to the possibilistic structure without loss of evidential semantics.

axioms (1)
  • domain assumption The isopignistic principle yields a reversible mapping between belief functions and possibilistic structures on the power set.
    Invoked in the abstract as the derivation source for the transformation.
invented entities (1)
  • belief evolution network no independent evidence
    purpose: To explicitly characterize relationships among subsets in the possibilistic structure.
    Introduced as part of the transformation to provide flexible representation beyond mass functions.

pith-pipeline@v0.9.0 · 5682 in / 1236 out tokens · 20797 ms · 2026-05-19T20:04:17.911100+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages

  1. [1]

    R. R. Yager and L. Liu,Classic works of the Dempster-Shafer theory of belief functions. Springer, 2008, vol. 219

    work page 2008

  2. [2]

    Multimodal measurement framework for thunderstorm charge motion: Spatiotemporal sensor fusion with bayesian-optimized localization,

    X. Yang, H. Xing, F. Zhu, Y . Chen, D. Camacho, X. Dong, and W. Pedrycz, “Multimodal measurement framework for thunderstorm charge motion: Spatiotemporal sensor fusion with bayesian-optimized localization,”IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1–14, 2025

    work page 2025

  3. [3]

    A novel method for generating permutation mass functions using permutations to represent class bias,

    M. Li, L. Li, Z. Zhang, and Q. Zhang, “A novel method for generating permutation mass functions using permutations to represent class bias,” Expert Systems with Applications, vol. 319, p. 132004, 2026

    work page 2026

  4. [4]

    Assessment of digital economy development with the new multicriteria sorting method: Dcmsort,

    Y . Liang, J. Qin, and A. Ishizaka, “Assessment of digital economy development with the new multicriteria sorting method: Dcmsort,” Omega, vol. 132, p. 103224, 2025

    work page 2025

  5. [5]

    Bsc: Belief shift clustering,

    Z.-W. Zhang, Z.-G. Liu, A. Martin, and K. Zhou, “Bsc: Belief shift clustering,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 3, pp. 1748–1760, 2023

    work page 2023

  6. [6]

    Maximum likelihood evidential reasoning,

    J.-B. Yang and D.-L. Xu, “Maximum likelihood evidential reasoning,” Artificial Intelligence, vol. 340, p. 104289, 2025

    work page 2025

  7. [7]

    An information fusion model of mutual influ- ence between focal elements: A perspective on interference effects in dempster–shafer evidence theory,

    X. Gao and L. Pan, “An information fusion model of mutual influ- ence between focal elements: A perspective on interference effects in dempster–shafer evidence theory,”Information Fusion, vol. 124, p. 103286, 2025

    work page 2025

  8. [8]

    Eriue: Evidential reasoning-based influential users evaluation in social networks,

    T. Wen, Y .-w. Chen, T. abbas Syed, and T. Wu, “Eriue: Evidential reasoning-based influential users evaluation in social networks,”Omega, vol. 122, p. 102945, 2024

    work page 2024

  9. [9]

    Mase: Multi-attribute source estimator for epidemic transmission in complex networks,

    J. Zhao and K. H. Cheong, “Mase: Multi-attribute source estimator for epidemic transmission in complex networks,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3308–3320, 2024

    work page 2024

  10. [10]

    Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic bayesian network approach,

    T. Huang, T. Xiahou, J. Mi, H. Chen, H.-Z. Huang, and Y . Liu, “Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic bayesian network approach,”Reliability Engineering & System Safety, vol. 249, p. 110225, 2024

    work page 2024

  11. [11]

    A linguis- tic z-number rule-based modeling framework considering knowledge reliability based on evidential reasoning rule,

    Z. Lian, Z. Zhou, C. Hu, P. Ning, Z. Ming, and X. Liu, “A linguis- tic z-number rule-based modeling framework considering knowledge reliability based on evidential reasoning rule,”IEEE Transactions on Knowledge and Data Engineering, 2025

    work page 2025

  12. [12]

    A dynamic reliability assessment method for multi-state manufacturing system by merging imprecise observational information,

    T. Huang, Q. Zhang, M. Beer, Y . Liu, and H.-Z. Huang, “A dynamic reliability assessment method for multi-state manufacturing system by merging imprecise observational information,”Reliability Engineering & System Safety, p. 111722, 2025

    work page 2025

  13. [13]

    Integration of multi-kinds imputation with covariance adaptation based on evidence theory,

    L. Huang, J. Fan, and A. W.-C. Liew, “Integration of multi-kinds imputation with covariance adaptation based on evidence theory,”IEEE Transactions on Neural Networks and Learning Systems, 2024, doi: 10.1109/TNNLS.2024.3412371

    work page doi:10.1109/tnnls.2024.3412371 2024

  14. [14]

    Incomplete data classification via distribution alignment with evidence combination,

    L. Huang, J. Fan, S. Wang, G. Liu, and A. W.-C. Liew, “Incomplete data classification via distribution alignment with evidence combination,” Machine Intelligence Research, pp. 1–23, 2026

    work page 2026

  15. [15]

    Deceptikang2024deceptiveve evidence detec- tion in information fusion of belief functions based on reinforcement learning,

    B. Kang and C. Zhao, “Deceptikang2024deceptiveve evidence detec- tion in information fusion of belief functions based on reinforcement learning,”Information Fusion, vol. 103, p. 102102, 2024

    work page 2024

  16. [16]

    A generalized belief dissimilarity measure based on weighted conflict belief and distance metric and its application in multi-source data fusion,

    M. Zhou, Y .-J. Zhou, J.-B. Yang, and J. Wu, “A generalized belief dissimilarity measure based on weighted conflict belief and distance metric and its application in multi-source data fusion,”Fuzzy Sets and Systems, vol. 475, p. 108719, 2024

    work page 2024

  17. [17]

    Cross and relative entropies of mass functions inspired by the plausibility entropy,

    X. Deng and W. Jiang, “Cross and relative entropies of mass functions inspired by the plausibility entropy,”Chinese Journal of Information Fusion, vol. 2, no. 3, pp. 212–222, 2025

    work page 2025

  18. [18]

    Quantum conflict measurement in decision fusion for out- of-distribution detection,

    Y . Dong, T. Zhu, X. Li, J. Dezert, R. Zhou, C. Zhu, L. Cao, and S. S. Ge, “Quantum conflict measurement in decision fusion for out- of-distribution detection,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

    work page 2026

  19. [19]

    The application of the matrix calculus to belief functions,

    P. Smets, “The application of the matrix calculus to belief functions,” International Journal of Approximate Reasoning, vol. 31, no. 1-2, pp. 1–30, 2002

    work page 2002

  20. [20]

    On theα-conjunctions for combining belief functions,

    F. Pichon, “On theα-conjunctions for combining belief functions,” in Belief Functions: Theory and Applications: Proceedings of the 2nd International Conference on Belief Functions, Compi `egne, France 9-11 May 2012. Springer, 2012, pp. 285–292

    work page 2012

  21. [21]

    Prejudice in uncertain information merging: Pushing the fusion paradigm of evidence theory further,

    D. Dubois, F. Faux, and H. Prade, “Prejudice in uncertain information merging: Pushing the fusion paradigm of evidence theory further,” International Journal of Approximate Reasoning, vol. 121, pp. 1–22, 2020

    work page 2020

  22. [22]

    Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence,

    T. Denœux, “Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence,”Artificial Intelligence, vol. 172, no. 2-3, pp. 234–264, 2008

    work page 2008

  23. [23]

    Basic belief assignment determination based on radial basis function network,

    W. Li, D. Han, J. Dezert, and Y . Yang, “Basic belief assignment determination based on radial basis function network,”Chinese Journal of Information Fusion, vol. 1, no. 3, pp. 175–182, 2024

    work page 2024

  24. [24]

    Z-number generation model and its application in a rule-based classi- fication system,

    Y . Li, J. A. Morente-Molinera, J. R. Trillo, and E. Herrera-Viedma, “Z-number generation model and its application in a rule-based classi- fication system,”IEEE Transactions on Cybernetics, 2025

    work page 2025

  25. [25]

    Idempotent conjunctive combination of belief functions: Extending the minimum rule of possibility theory,

    S. Destercke and D. Dubois, “Idempotent conjunctive combination of belief functions: Extending the minimum rule of possibility theory,” Information Sciences, vol. 181, no. 18, pp. 3925–3945, 2011

    work page 2011

  26. [26]

    Modeling belief propensity degree: Measures of evenness and diversity of belief functions,

    Q. Zhou, ´E. Boss ´e, and Y . Deng, “Modeling belief propensity degree: Measures of evenness and diversity of belief functions,”IEEE Transac- tions on Systems, Man, and Cybernetics: Systems, 2022

    work page 2022

  27. [27]

    New semantics for quantitative pos- sibility theory,

    D. Dubois, H. Prade, and P. Smets, “New semantics for quantitative pos- sibility theory,” in6th European Conference, ECSQARU 2001 Toulouse, France, September 19–21, 2001 Proceedings 6. Springer, 2001, pp. 410–421

    work page 2001

  28. [28]

    A definition of subjective possibility,

    ——, “A definition of subjective possibility,”International journal of approximate reasoning, vol. 48, no. 2, pp. 352–364, 2008

    work page 2008

  29. [29]

    La th ´eorie des possibilit ´es quantitatives ´epist´emiques vue comme un modele de croyances transf ´erables tres prudent

    P. Smets, “La th ´eorie des possibilit ´es quantitatives ´epist´emiques vue comme un modele de croyances transf ´erables tres prudent.”LFA La Rochelle, pp. 343–353, 2000

    work page 2000

  30. [30]

    Belief evolution network-based prob- ability transformation and fusion,

    Q. Zhou, Y . Huang, and Y . Deng, “Belief evolution network-based prob- ability transformation and fusion,”Computers & Industrial Engineering, vol. 174, p. 108750, 2022

    work page 2022

  31. [31]

    Isopignistic canonical decomposition via belief evolution network,

    Q. Zhou, T. Zhan, and Y . Deng, “Isopignistic canonical decomposition via belief evolution network,”arXiv preprint arXiv:2405.02653, 2024

    work page arXiv 2024

  32. [32]

    A dependence assessment method based on quantum model of mass function in human reliability analysis,

    X. Su, X. Huang, X. Pan, and D. Meng, “A dependence assessment method based on quantum model of mass function in human reliability analysis,”Expert Systems with Applications, vol. 299, p. 129992, 2026

    work page 2026

  33. [33]

    Ternary coding of maximum deng entropy,

    T. Zhan, Y . He, and Y . Deng, “Ternary coding of maximum deng entropy,”Fuzzy Sets and Systems, p. 109913, 2026

    work page 2026

  34. [34]

    A consistency-specificity trade- off to select source behavior in information fusion,

    F. Pichon, S. Destercke, and T. Burger, “A consistency-specificity trade- off to select source behavior in information fusion,”IEEE transactions on cybernetics, vol. 45, no. 4, pp. 598–609, 2014

    work page 2014

  35. [35]

    Individual linguis- tic granular computing: A granulation–degranulation-based approach,

    T. Huang, Q. Zhang, W. Pedrycz, and S. Yang, “Individual linguis- tic granular computing: A granulation–degranulation-based approach,” IEEE Transactions on Cybernetics, 2026

    work page 2026

  36. [36]

    On decombination of belief func- tion,

    D. Han, Y . Yang, and J. Dezert, “On decombination of belief func- tion,” in2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019, pp. 1–6

    work page 2019

  37. [37]

    The canonical decomposition of a weighted belief,

    P. Smets, “The canonical decomposition of a weighted belief,” inIJCAI, vol. 95, 1995, pp. 1896–1901

    work page 1995

  38. [38]

    Canonical decomposition of belief functions based on teugels’ representation of the multivariate bernoulli distribution,

    F. Pichon, “Canonical decomposition of belief functions based on teugels’ representation of the multivariate bernoulli distribution,”Infor- mation Sciences, vol. 428, pp. 76–104, 2018

    work page 2018

  39. [39]

    The basic principles of uncertain information fusion. an organised review of merging rules in different representation frameworks,

    D. Dubois, W. Liu, J. Ma, and H. Prade, “The basic principles of uncertain information fusion. an organised review of merging rules in different representation frameworks,”Information Fusion, vol. 32, pp. 12–39, 2016

    work page 2016

  40. [40]

    Combination in the theory of evidence via a new measurement of the conflict between evidences,

    J. Abell ´an, S. Moral-Garc ´ıa, and M. D. Ben ´ıtez, “Combination in the theory of evidence via a new measurement of the conflict between evidences,”Expert Systems with Applications, vol. 178, p. 114987, 2021

    work page 2021

  41. [41]

    Idempotent conjunctive and disjunctive combination of belief functions by distance minimization,

    J. Klein, S. Destercke, and O. Colot, “Idempotent conjunctive and disjunctive combination of belief functions by distance minimization,” International Journal of Approximate Reasoning, vol. 92, pp. 32–48, 2018

    work page 2018

  42. [42]

    On the dempster-shafer framework and new combination rules,

    R. R. Yager, “On the dempster-shafer framework and new combination rules,”Information sciences, vol. 41, no. 2, pp. 93–137, 1987

    work page 1987

  43. [43]

    Complex evidence theory for multisource data fusion,

    F. Xiao, J. Wen, W. Pedrycz, and M. Aritsugi, “Complex evidence theory for multisource data fusion,”Chinese Journal of Information Fusion, vol. 1, no. 2, pp. 134–159, 2024

    work page 2024

  44. [44]

    Esurvfusion: An evidential multimodal survival fusion model based on epistemic random fuzzy sets,

    L. Huang, Y . Xing, Q. Lin, J. Duan, S. Ruan, and M. Feng, “Esurvfusion: An evidential multimodal survival fusion model based on epistemic random fuzzy sets,”IEEE Transactions on Fuzzy Systems, 2025

    work page 2025