An experimental comparison of performance measures for classification
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Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The F-score is a notable metric for imbalanced classification.
“The F-score is extensively utilized not just because of its expressive capacity but also because it is a notable metric for imbalanced classification [32].”
From Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm (2024) · cites this paper as [32]
Cites
Using AUC and accuracy in evaluating learning algorithms
Huang, J., & Ling, C. X. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.50 · Cited by 2,148 · see citing works
The Comparison and Evaluation of Forecasters
DeGroot, M. H., & Fienberg, S. E. (1983) · The Statistician · doi:10.2307/2987588 · Cited by 604 · see citing works
Fawcett, T., & Niculescu-Mizil, A. (2007) · Machine Learning · doi:10.1007/s10994-007-5011-0 · Cited by 109 · see citing works
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×
“This metric is also known as Brier score (Brier, 1950) and integrates calibration and other components, usually grouped under the term ‘refinement’ MSE ¼ Pc j¼1 Pm i¼1ðf ði; jÞ pði; jÞÞ 2 m c : LogLoss: (LogL).”
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Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143 · see citing works
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129 · see citing works
A Comparison of MCC and CEN Error Measures in Multi-Class Prediction
Jurman, G., Riccadonna, S., & Furlanello, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041882 · Cited by 406 · see citing works
Why Cohen’s Kappa should be avoided as performance measure in classification
Delgado, R., & Tibau, X.-A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0222916 · Cited by 324 · see citing works
A review of the F-measure: its history, properties, criticism, and alternatives
Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236 · see citing works
Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2019.1636086 · Cited by 124 · see citing works
Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works
On the appropriateness of Platt scaling in classifier calibration
Böken, B. (2021) · Information Systems · doi:10.1016/j.is.2020.101641 · Cited by 70 · see citing works
ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification
Li, M., Xiong, A., Wang, L., Deng, S., & Ye, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105818 · Cited by 66 · see citing works
Missing the missing values: The ugly duckling of fairness in machine learning
Fernando, M., Cèsar, F., David, N., & José, H. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22415 · Cited by 62 · see citing works
Insider threat detection model using anomaly-based Isolation Forest algorithm
Al-Shehari, T., Al-Razgan, M., Alfakih, T., Alsowail, R. A., & Pandiaraj, S. (2023) · IEEE Access · doi:10.1109/access.2023.3326750 · Cited by 52 · see citing works
Al-Shehari, T. A., Rosaci, D., Al-Razgan, M., Alfakih, T., Kadrie, M., Afzal, H., & Nawaz, R. (2024) · IEEE Access · doi:10.1109/access.2024.3373694 · Cited by 51 · see citing works
“The F-score is extensively utilized not just because of its expressive capacity but also because it is a notable metric for imbalanced classification [32].”
BackgroundOn the effect of calibration in classifier combination
Bella, A., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2013) · Applied Intelligence · doi:10.1007/s10489-012-0388-2 · Cited by 45 · see citing works
Self-citationDeepTaskAPT: Insider APT detection using Task-tree based Deep Learning
Mamun, M., & Shi, K. (2021) · 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00102 · Cited by 29 · see citing works
Gulzar, Q., & Mustafa, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-89650-5 · Cited by 29 · see citing works
MIPHENO: data normalization for high throughput metabolite analysis
Bell, S. M., Burgoon, L. D., & Last, R. L. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-10 · Cited by 17 · see citing works
- Authors
- Ferri, C., Hernández-Orallo, J., & Modroiu, R.
- Affiliations
- Universitat Politècnica de València (ES)
- Corresponding author
- Ferri, C.
- ORCID
- Ferri, C.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2009-01
- Volume
- 30
- Issue
- 1
- Pages
- 27-38
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2116825089
- Cited by
- 889
- References
- 4
- Citations by year
- 2026: 44 · 2025: 61 · 2024: 89 · 2023: 91 · 2022: 99 · 2021: 95 · 2020: 73 · 2019: 64 · 2018: 56 · 2017: 35 · 2016: 23 · 2015: 32 · 2014: 24 · 2013: 30 · 2012: 26
- FWCI
- 24.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition Letters (publishedVersion)
- Funders
- Ministerio de Economía y Competitividad
- Generalitat Valenciana
- 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 › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- classification performance measures, evaluation metrics, sensitivity analysis, separability, ranking quality, calibration
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 ›
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