Guaranteeing consistency in evidence fusion: A novel perspective on credibility
- URL: http://arxiv.org/abs/2504.04128v1
- Date: Sat, 05 Apr 2025 10:12:32 GMT
- Title: Guaranteeing consistency in evidence fusion: A novel perspective on credibility
- Authors: Chaoxiong Ma, Yan Liang, Huixia Zhang, Hao Sun,
- Abstract summary: It is explored that available credible evidence fusion schemes suffer from the potential inconsistency because credibility calculation and Dempster's combination rule-based fusion are sequentially performed in an open-loop style.<n>This paper proposes an iterative credible evidence fusion (ICEF) to overcome the inconsistency in view of close-loop control.
- Score: 7.626019758281367
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is explored that available credible evidence fusion schemes suffer from the potential inconsistency because credibility calculation and Dempster's combination rule-based fusion are sequentially performed in an open-loop style. This paper constructs evidence credibility from the perspective of the degree of support for events within the framework of discrimination (FOD) and proposes an iterative credible evidence fusion (ICEF) to overcome the inconsistency in view of close-loop control. On one hand, the ICEF introduces the fusion result into credibility assessment to establish the correlation between credibility and the fusion result. On the other hand, arithmetic-geometric divergence is promoted based on the exponential normalization of plausibility and belief functions to measure evidence conflict, called plausibility-belief arithmetic-geometric divergence (PBAGD), which is superior in capturing the correlation and difference of FOD subsets, identifying abnormal sources, and reducing their fusion weights. The ICEF is compared with traditional methods by combining different evidence difference measure forms via numerical examples to verify its performance. Simulations on numerical examples and benchmark datasets reflect the adaptability of PBAGD to the proposed fusion strategy.
Related papers
- TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.
Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
arXiv Detail & Related papers (2025-04-20T09:20:55Z) - A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making [16.67762257337951]
A privacy-preserving distributed credible evidence fusion method with three-level consensus (PCEF) is proposed in this paper.<n>The simulations show that PCEF is close to CCEF both in credibility and fusion results and obtains higher decision accuracy with less time-comsuming than existing methods.
arXiv Detail & Related papers (2024-12-03T03:36:42Z) - Credible fusion of evidence in distributed system subject to cyberattacks [2.5539863252714636]
This paper proposes an algorithm for credible evidence fusion against cyberattacks.<n>We focus on three requirements for evidence fusion, i.e., preserving evidence's privacy, identifying attackers and excluding their evidence.<n>The states of normal nodes are shown to converge to their WAVCCME, while the attacker's evidence is excluded from the fusion.
arXiv Detail & Related papers (2024-11-29T13:46:04Z) - Evaluating Evidential Reliability In Pattern Recognition Based On Intuitionistic Fuzzy Sets [9.542461785588925]
We propose an algorithm for quantifying the reliability of evidence sources, called Fuzzy Reliability Index (FRI)
The FRI algorithm is based on decision quantification rules derived from IFS, defining the contribution of different BPAs to correct decisions and deriving the evidential reliability from these contributions.
The proposed method effectively enhances the rationality of reliability estimation for evidence sources, making it particularly suitable for classification decision problems in complex scenarios.
arXiv Detail & Related papers (2024-10-30T08:05:26Z) - Rectified Diffusion Guidance for Conditional Generation [62.00207951161297]
We revisit the theory behind CFG and rigorously confirm that the improper configuration of the combination coefficients (i.e., the widely used summing-to-one version) brings about expectation shift of the generative distribution.
We propose ReCFG with a relaxation on the guidance coefficients such that denoising with ReCFG strictly aligns with the diffusion theory.
That way the rectified coefficients can be readily pre-computed via traversing the observed data, leaving the sampling speed barely affected.
arXiv Detail & Related papers (2024-10-24T13:41:32Z) - Inter Observer Variability Assessment through Ordered Weighted Belief Divergence Measure in MAGDM Application to the Ensemble Classifier Feature Fusion [1.3586572110652486]
A large number of multi-attribute group decisionmaking (MAGDM) have been widely introduced to obtain consensus results.
This study aims to propose an Evidential MAGDM method by assessing the inter-observational variability and handling uncertainty.
arXiv Detail & Related papers (2024-09-13T00:53:00Z) - Evidential Deep Partial Multi-View Classification With Discount Fusion [24.139495744683128]
We propose a novel framework called Evidential Deep Partial Multi-View Classification (EDP-MVC)
We use K-means imputation to address missing views, creating a complete set of multi-view data.
The potential conflicts and uncertainties within this imputed data can affect the reliability of downstream inferences.
arXiv Detail & Related papers (2024-08-23T14:50:49Z) - ELFNet: Evidential Local-global Fusion for Stereo Matching [17.675146012208124]
We introduce the textbfEvidential textbfLocal-global textbfFusion (ELF) framework for stereo matching.
It endows both uncertainty estimation and confidence-aware fusion with trustworthy heads.
arXiv Detail & Related papers (2023-08-01T15:51:04Z) - FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof [60.990541463214605]
Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other.
We propose a provable secure model ownership verification scheme using zero-knowledge proof, named FedZKP.
arXiv Detail & Related papers (2023-05-08T07:03:33Z) - Reliable Federated Disentangling Network for Non-IID Domain Feature [62.73267904147804]
In this paper, we propose a novel reliable federated disentangling network, termed RFedDis.
To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling.
Our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.
arXiv Detail & Related papers (2023-01-30T11:46:34Z) - Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC)
TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
arXiv Detail & Related papers (2022-04-25T03:48:49Z) - Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma
Distributions [91.63716984911278]
We introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result.
Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks.
arXiv Detail & Related papers (2021-11-11T14:28:12Z) - BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable.
We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
arXiv Detail & Related papers (2021-06-07T10:14:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.