The DOI matches its Crossref record.
Combination of Evidence in Dempster-Shafer Theory
Open accessAbstract
Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Robust optimization – A comprehensive survey
Beyer, H.-G., & Sendhoff, B. (2007) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2007.03.003 · Cited by 1,594
Risks and Decisions for Conservation and Environmental Management
Burgman, M. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511614279 · Cited by 498
Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning
Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2009) · Information Fusion · doi:10.1016/j.inffus.2008.04.001 · Cited by 347
Uncertainty quantification using evidence theory in multidisciplinary design optimization
Agarwal, H., Renaud, J. E., Preston, E. L., & Padmanabhan, D. (2004) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2004.03.017 · Cited by 324
Shahriar, A., Sadiq, R., & Tesfamariam, S. (2012) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2011.12.007 · Cited by 306
Metadata
- Authors
- SENTZ, K., & FERSON, S.
- Published
- 2002-04-01
- OpenAlex
- W1600834507
- Cited by
- 1,093
- References
- 0
- Type
- report
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Combination of Evidence in Dempster-Shafer Theory. https://app.cochat.ai/papers/10.2172/800792
- The paper
- SENTZ, K., & FERSON, S. (2002). Combination of Evidence in Dempster-Shafer Theory. https://doi.org/10.2172/800792
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
