Computing Precision and Recall with Missing or Uncertain Ground Truth
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The method by [22] cannot be used for data sets with partial ground truth.
“An attempt to compute objective metrics, such as precision and recall with missing GT is proposed by [22] but it cannot be used for data sets with partial GT since it applies a probabilistic model under the same assumptions.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [22]
Cites
A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation
Goutte, C., & Gaussier, E. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31865-1_25 · Cited by 2,285 · see citing works
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Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2665165 · Cited by 152 · see citing works
- Authors
- Lamiroy, B., & Sun, T.
- Affiliations
- Laboratoire Lorrain de Recherche en Informatique et ses Applications (FR)
- Lehigh University (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 7423
- Pages
- 149-162
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W341877867
- Cited by
- 22
- References
- 1
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2020: 2 · 2019: 1 · 2018: 4 · 2017: 5 · 2016: 1 · 2015: 3 · 2014: 1
- FWCI
- 1.08
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- precision, memory retrieval, probabilistic interpretation, method comparison, systematic bias
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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