The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
PAV and the ROC convex hull
Open accessOverview
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
Cites
Measuring the Accuracy of Diagnostic Systems
Swets, J. A. (1988) · Science · doi:10.1126/science.3287615 · Cited by 10,058
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,834
An empirical comparison of supervised learning algorithms
Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143865 · Cited by 2,780
Self-citationAdvances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,910
Predicting good probabilities with supervised learning
Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884
Self-citationTransforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
Robust classification for imprecise environments
Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250
Self-citationAn Empirical Distribution Function for Sampling with Incomplete Information
Ayer, M., Brunk, H. D., Ewing, G. M., Reid, W. T., & Silverman, E. (1955) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728423 · Cited by 896
Better Decisions through Science
Swets, J. A., Dawes, R. M., & Monahan, J. C. (2000) · Scientific American · doi:10.1038/scientificamerican1000-82 · Cited by 569
Learning and making decisions when costs and probabilities are both unknown
Zadrozny, B., & Elkan, C. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502540 · Cited by 465
Cited by
Sorted by citations
Items 101–109 of 109
IDS Evaluation Approach Based on MLE-ROC
Liang, H., Chang, J., & Li, K. (2010) · WRI Global Congress on Intelligent Systems · doi:10.1109/gcis.2010.227
Semi-supervised Bayesian learning
Guo, Y. (2012)
Noble Ape’s Cognitive Simulation
Barbalet, T. S. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch709
Independence and maximal volume of d-dimensional random convex hull
Son, W., Park, S., & Lim, J. (2018) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2018.25.1.079
Evaluating Calibration Techniques for Reliable Predictions
Maalej, A., Johansson, U., & Löfström, T. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6403-0_14
Promoting Fairness in Information Access Within Social Networks
Liu, C., Zhou, X., Zehmakan, A. N., & Zhang, Z. (2026) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65706.2026.00038
Aide à la modélisation et au traitement de données massives : proposition d'un guide méthodologique
Maximilien, D. (2019) · theses.fr (ABES) · doi:10.70675/bf42da9bzb52ez47dcza8b4zc7ecc6528b7a
An Evaluation of Calibration Methods for Data Mining Models in Simulation Problems
Sanjuán, A. B. (2011)
Metadata
- Authors
- Fawcett, T., & Niculescu-Mizil, A.
- Affiliations
- Stanford University (US)
- Cornell University (US)
- Corresponding author
- Fawcett, Tom
- ORCID
- Fawcett, Tom
- Venue
- Machine Learning
- Published
- 2007-05-15
- Volume
- 68
- Issue
- 1
- Pages
- 97-106
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2166290812
- Cited by
- 109
- References
- 13
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 2 · 2021: 7 · 2020: 4 · 2019: 5 · 2018: 5 · 2017: 7 · 2016: 11 · 2015: 6 · 2014: 6 · 2013: 11 · 2012: 9
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Funders
- National Science Foundation (0412930)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- classifier calibration, isotonic regression, pool-adjacent-violators algorithm, probability estimation
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
