The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning
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Citations
Cites
(1996) · Cited by 20,179
A Mathematical Theory of Evidence
Shafer, G. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,711
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
A mathematical theory of evidence
Rota, G. (1977) · Advances in Mathematics · doi:10.1016/0001-8708(77)90069-x · Cited by 1,802
Statistical Fraud Detection: A Review
Bolton, R. J., & Hand, D. J. (2002) · Statistical Science · doi:10.1214/ss/1042727940 · Cited by 1,450
Combination of Evidence in Dempster-Shafer Theory
SENTZ, K., & FERSON, S. (2002) · doi:10.2172/800792 · Cited by 1,093
The base-rate fallacy and the difficulty of intrusion detection
Axelsson, S. (2000) · ACM Transactions on Information and System Security · doi:10.1145/357830.357849 · Cited by 638
Distributed data mining in credit card fraud detection
Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (1999) · IEEE Intelligent Systems · doi:10.1109/5254.809570 · Cited by 538
Identifying the signs of fraudulent accounts using data mining techniques
Li, S.-H., Yen, D. C., Lu, W.-H., & Wang, C. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.01.002 · Cited by 505
Elicitation of Expert Opinions for Uncertainty and Risks
Ayyub, B. M. (2001) · CRC Press eBooks · doi:10.1201/9781420040906 · Cited by 450
Cited by
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
Intelligent financial fraud detection: A comprehensive review
West, J., & Bhattacharya, M. (2016) · Computers & Security · doi:10.1016/j.cose.2015.09.005 · Cited by 592
Feature engineering strategies for credit card fraud detection
Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.12.030 · Cited by 451
Transaction aggregation as a strategy for credit card fraud detection
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0116-z · Cited by 337
Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021) · Information Sciences · doi:10.1016/j.ins.2019.05.023 · Cited by 283
Metadata
- Authors
- Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2009-10
- Volume
- 10
- Issue
- 4
- Pages
- 354-363
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W1964089535
- Cited by
- 347
- References
- 23
- FWCI
- 11.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. https://app.cochat.ai/papers/10.1016/j.inffus.2008.04.001
- The paper
- Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2009). Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion. https://doi.org/10.1016/j.inffus.2008.04.001
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.
