The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
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Citations
Cites
Machine learning as an experimental science
Langley, P. (1988) · Machine Learning · doi:10.1007/bf00115008 · Cited by 187
Cited by
Logistic regression and Artificial Neural Network Classification models: A methodology review
Dreiseitl, S., & Ohno-Machado, L. (2002) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(03)00034-0 · Cited by 2,177
Evaluation metrics and statistical tests for machine learning
Rainio, O., Teuho, J., & Klén, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-56706-x · Cited by 1,338
Robust classification for imprecise environments
Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., & Keogh, E. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911747 · Cited by 1,128
Classifier Technology and the Illusion of Progress
Hand, D. J. (2006) · Statistical Science · doi:10.1214/088342306000000060 · Cited by 723
Metadata
- Authors
- Salzberg, S. L.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 1997-09
- Volume
- 1
- Issue
- 3
- Pages
- 317-328
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2112865076
- Cited by
- 827
- References
- 1
- FWCI
- 14.74
- Type
- journal article
- Language
- en
- Open access
- closed
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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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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. https://app.cochat.ai/papers/10.1023/a:1009752403260
- The paper
- Salzberg, S. L. (1997). On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery. https://doi.org/10.1023/a%3A1009752403260
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
