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
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Cites
Measuring the Accuracy of Diagnostic Systems
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Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
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An empirical comparison of supervised learning algorithms
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Self-citationAdvances in Large-Margin Classifiers
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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
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Self-citationAn Empirical Distribution Function for Sampling with Incomplete Information
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Learning and making decisions when costs and probabilities are both unknown
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Repairing concavities in ROC curves
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The Synergy Between PAV and AdaBoost
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VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
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Verification of forecasts expressed in terms of probability
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“1 Brier Score is used to assess the quality of the probabilities predicted by a classifier (Brier, 1950).”
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An experimental comparison of performance measures for classification
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.08.010 · Cited by 889
Machine Learning: The art and science of algorithms that make sense of data
Flach, P. A. (2012) · Cited by 694
Precision-Recall-Gain Curves: PR Analysis Done Right
Flach, P. A., & Kull, M. (2015) · Bristol Research (University of Bristol) · doi:10.5555/2969239.2969333 · Cited by 289
Classifier calibration: a survey on how to assess and improve predicted class probabilities
Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023) · Machine Learning · doi:10.1007/s10994-023-06336-7 · Cited by 228
Hernández‐Orallo, J., Flach, P. A., & Ferri, C. (2012) · Bristol Research (University of Bristol) · Cited by 181
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
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