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Study on the Impact of Partition-Induced Dataset Shift on <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>-Fold Cross-Validation
Abstract
Cross-validation is a very commonly employed technique used to evaluate classifier performance. However, it can potentially introduce dataset shift, a harmful factor that is often not taken into account and can result in inaccurate performance estimation. This paper analyzes the prevalence and impact of partition-induced covariate shift on different k-fold cross-validation schemes. From the experimental results obtained, we conclude that the degree of partition-induced covariate shift depends on the cross-validation scheme considered. In this way, worse schemes may harm the correctness of a single-classifier performance estimation and also increase the needed number of repetitions of cross-validation to reach a stable performance estimation.
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Citations
Cites
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
Using AUC and accuracy in evaluating learning algorithms
Huang, J., & Ling, C. X. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.50 · Cited by 2,148
Cited by
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
Metadata
- Authors
- Moreno-Torres, J. G., Saez, J. A., & Herrera, F.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2012-08
- Volume
- 23
- Issue
- 8
- Pages
- 1304-1312
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2085770564
- Cited by
- 306
- References
- 3
- FWCI
- 13.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- Moreno-Torres, J. G., Saez, J. A., & Herrera, F. (2012). Study on the Impact of Partition-Induced Dataset Shift on <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>-Fold Cross-Validation. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2012.2199516
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