The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Adaptive Fraud Detection
Overview
The claims and statistics of this paper haven’t been checked yet.
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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
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Citations
Cites
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Self-citationLOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Metadata
- Authors
- Fawcett, T., & Provost, F.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 1997-09
- Volume
- 1
- Issue
- 3
- Pages
- 291-316
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W1517113043
- Cited by
- 881
- References
- 2
- FWCI
- 14.74
- Type
- journal article
- Language
- en
- Open access
- closed
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Adaptive Fraud Detection. https://app.cochat.ai/papers/10.1023/a:1009700419189
- The paper
- Fawcett, T., & Provost, F. (1997). Adaptive Fraud Detection. Data Mining and Knowledge Discovery. https://doi.org/10.1023/a%3A1009700419189
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.
