The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms
Abstract
Dietterich (1998) reviews five statistical tests and proposes the 5 × 2 cvt test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5 × 2 cvt test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 × 2 cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower type I error and higher power than 5 × 2 cv proper.
Overview
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
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
Introduction to Probability and Statistics for Engineers and Scientists
Reynolds, M. R. (1988) · Technometrics · doi:10.1080/00401706.1988.10488450 · Cited by 989
Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms
Inza, I., Larrañaga, P., & Sierra, B. (2001) · International Journal of Approximate Reasoning · doi:10.1016/s0888-613x(01)00038-x · Cited by 323
Feature Subset Selection by Bayesian network-based optimization
Inza, I., Larrañaga, P., Etxeberria, R., & Sierra, B. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(00)00052-7 · Cited by 234
Minimal approximate hitting sets and rule templates
Vinterbo, S. A., & Øhrn, A. (2000) · International Journal of Approximate Reasoning · doi:10.1016/s0888-613x(00)00051-7 · Cited by 145
Cited by
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
Reliable Accuracy Estimates from k-Fold Cross Validation
Wong, T.-T., & Yeh, P.-Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2912815 · Cited by 1,074
Machine learning in bioinformatics
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J. A., Armañanzas, R., Santafé, G., Pérez, A., et al. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbk007 · Cited by 867
Mining data with random forests: A survey and results of new tests
Verikas, A., Gelzinis, A., & Bacauskiene, M. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2010.08.011 · Cited by 719
(2011) · Cited by 675
Metadata
- Authors
- Alpaydm, E.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1999-11-01
- Volume
- 11
- Issue
- 8
- Pages
- 1885-1892
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2150706391
- Cited by
- 431
- References
- 6
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms. https://app.cochat.ai/papers/10.1162/089976699300016007
- The paper
- Alpaydm, E. (1999). Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms. Neural Computation. https://doi.org/10.1162/089976699300016007
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.
