Cited by 1,974Imbalanced Data Classification TechniquesMachine Learning and Data ClassificationData Mining and Machine Learning Applications

Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation

Sokolova, Marina, Japkowicz, Nathalie, Szpakowicz, Stan·2006·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/11941439_114

Cites

26 of 26 references verified0 citation errors · 0 not checkable

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  • ML practitioners can measure performance in a multitude of ways, and there has been much discussion concerning the choice of different performance measures and approaches.

    “ML practitioners can measure performance in a multitude of ways, and there has been much discussion concerning the choice of diferent performance measures and approaches [84,267].”

    From Fairness in machine learning: A survey (2024) · cites this paper as [267]

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Authors
Sokolova, M., Japkowicz, N., & Szpakowicz, S.
Affiliations
  • Université de Montréal (CA)
  • University of Ottawa (CA)
Venue
Lecture Notes in Computer Science
Published
2006
Pages
1015-1021
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W172260869
Cited by
1,974
References
26
Citations by year
2026: 66 · 2025: 192 · 2024: 241 · 2023: 229 · 2022: 235 · 2021: 260 · 2020: 184 · 2019: 133 · 2018: 84 · 2017: 68 · 2016: 54 · 2015: 47 · 2014: 54 · 2013: 31 · 2012: 29
FWCI
5.40
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
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
classifier evaluation, Youden index, likelihood, discriminatory power, failure prevention, diagnosis, electronic negotiation, ROC curve, F-score, accuracy

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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.

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