Abstract
Motivation: It is commonly assumed in pattern recognition that cross-validation error estimation is 'almost unbiased' as long as the number of folds is not too small. While this is true for random sampling, it is not true with separate sampling, where the populations are independently sampled, which is a common situation in bioinformatics. Results: We demonstrate, via analytical and numerical methods, that classical cross-validation can have strong bias under separate sampling, depending on the difference between the sampling ratios and the true population probabilities. We propose a new separate-sampling cross-validation error estimator, and prove that it satisfies an 'almost unbiased' theorem similar to that of random-sampling cross-validation. We present two case studies with previously published data, which show that the results can change drastically if the correct form of cross-validation is used.
| Original language | English |
|---|---|
| Pages (from-to) | 3349-3355 |
| Number of pages | 7 |
| Journal | Bioinformatics |
| Volume | 30 |
| Issue number | 23 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Computational Theory and Mathematics
- Computer Science Applications
- Computational Mathematics
- Statistics and Probability
Cite this
Cross-validation under separate sampling : Strong bias and how to correct it. / Braga-Neto, Ulisses M.; Zollanvari, Amin; Dougherty, Edward R.
In: Bioinformatics, Vol. 30, No. 23, 2014, p. 3349-3355.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Cross-validation under separate sampling
T2 - Strong bias and how to correct it
AU - Braga-Neto, Ulisses M.
AU - Zollanvari, Amin
AU - Dougherty, Edward R.
PY - 2014
Y1 - 2014
N2 - Motivation: It is commonly assumed in pattern recognition that cross-validation error estimation is 'almost unbiased' as long as the number of folds is not too small. While this is true for random sampling, it is not true with separate sampling, where the populations are independently sampled, which is a common situation in bioinformatics. Results: We demonstrate, via analytical and numerical methods, that classical cross-validation can have strong bias under separate sampling, depending on the difference between the sampling ratios and the true population probabilities. We propose a new separate-sampling cross-validation error estimator, and prove that it satisfies an 'almost unbiased' theorem similar to that of random-sampling cross-validation. We present two case studies with previously published data, which show that the results can change drastically if the correct form of cross-validation is used.
AB - Motivation: It is commonly assumed in pattern recognition that cross-validation error estimation is 'almost unbiased' as long as the number of folds is not too small. While this is true for random sampling, it is not true with separate sampling, where the populations are independently sampled, which is a common situation in bioinformatics. Results: We demonstrate, via analytical and numerical methods, that classical cross-validation can have strong bias under separate sampling, depending on the difference between the sampling ratios and the true population probabilities. We propose a new separate-sampling cross-validation error estimator, and prove that it satisfies an 'almost unbiased' theorem similar to that of random-sampling cross-validation. We present two case studies with previously published data, which show that the results can change drastically if the correct form of cross-validation is used.
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U2 - 10.1093/bioinformatics/btu527
DO - 10.1093/bioinformatics/btu527
M3 - Article
VL - 30
SP - 3349
EP - 3355
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 23
ER -