The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1603.02754 · Cited by 7,104
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005
Cohen, W. W. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,890
Rotation Forest: A New Classifier Ensemble Method
Rodríguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.211 · Cited by 1,931
(2010) · doi:10.5555/1756006.1859921 · Cited by 1,924
Self-citationExtreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,922
Cited by
Sorted by citations
Items 301–310 of 310
Machine Learning–Based Optimization of Phthalate Extraction Conditions Using HS‐INME‐GC/MS Data
Jung, H., Bae, S., & Kim, J. (2026) · Journal of Chemometrics · doi:10.1002/cem.70181
Kar, S., Dwivedi, P., Post, C., Hagan, D. L., & Norman, S. P. (2026) · Trees Forests and People · doi:10.1016/j.tfp.2026.101506
Vithalani, A. A., Nayak, H. D., Patel, S. P., & Sheth, T. B. (2026) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-026-2951-8
Evaluation Integrity in Spatially Grouped Machine Learning: Lessons from Drillhole Grade EstimationPreprint
Maldeh, S. A., Mohammadzadeh, J., & Harifi, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7576916
Bi-SMOTE: bi-directional oversampling for reducing class overlap and synthetic noise
Sağlam, F., & Yamak, U. S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134648
Sarmiento-Rosales, S. M., Hernández, V. A. S., Monroy, R., & Toscano‐Pulido, G. (2026) · IEEE Access · doi:10.1109/access.2026.3741539
Khan, I., Khan, I., Noor, U., Irfan, M., & Zhiqiang, L. (2026) · Archives of Microbiology · doi:10.1007/s00203-026-05209-6
Stochastic Hierarchy Induction for Time Series Classification: Foundations and Benchmark
Alagöz, C. (2026) · Computational Intelligence · doi:10.1111/coin.70301
Nguyen, D. D., Quattrone, A., Paine, M., Fazlollahi, A., Lee, S., Wang, B. (., Davidson, T., Coleman, F., O’Sullivan, J. D., & Nestor, P. J. (2026) · European Journal of Neurology · doi:10.1111/ene.70790
Wijayanti, A. D., Rafrastara, F. A., Ghozi, W., Sani, R. R., Megantara, R. A., & Osman, W. M. (2026) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic71495.2026.11742610
Metadata
- Authors
- Wainer, J., & Cawley, G.
- Affiliations
- Universidade Estadual de Campinas (UNICAMP) (BR)
- University of East Anglia (GB)
- Corresponding author
- Wainer, Jacques
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 182
- Pages
- 115222
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2892606308
- Cited by
- 309
- References
- 25
- Citations by year
- 2026: 63 · 2025: 64 · 2024: 68 · 2023: 46 · 2022: 40 · 2021: 14
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- nested cross-validation, classifier selection, hyperparameter optimization, algorithm selection, binary classification, bias-variance trade-off, computational cost
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 ›
Are you an author, or do you see a problem with this page? Request a review
