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Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap

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Cited by:

  1. Matthew Tuson & Berwin Turlach & Kevin Murray & Mei Ruu Kok & Alistair Vickery & David Whyatt, 2021. "Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation," IJERPH, MDPI, vol. 18(19), pages 1-21, September.
  2. Chen, Weijie & Yousef, Waleed A. & Gallas, Brandon D. & Hsu, Elizabeth R. & Lababidi, Samir & Tang, Rong & Pennello, Gene A. & Symmans, W. Fraser & Pusztai, Lajos, 2012. "Uncertainty estimation with a finite dataset in the assessment of classification models," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1016-1027.
  3. Nikolaos Giannakis & Periklis Gogas & Theophilos Papadimitriou & Jamel Saadaoui & Emmanouil Sofianos, 2025. "Do International Reserve Holdings Still Predict Economic Crises? Insights from Recent Machine Learning Techniques," Working Papers 2025.6, International Network for Economic Research - INFER.
  4. John J Nay & Yevgeniy Vorobeychik, 2016. "Predicting Human Cooperation," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
  5. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
  6. Gianluca Gazzola & Myong K. Jeong, 2021. "Support vector regression for polyhedral and missing data," Annals of Operations Research, Springer, vol. 303(1), pages 483-506, August.
  7. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
  8. I. Charvet & A. Suppasri & H. Kimura & D. Sugawara & F. Imamura, 2015. "A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 2073-2099, December.
  9. Maggioni, Francesca & Spinelli, Andrea, 2025. "A novel robust optimization model for nonlinear Support Vector Machine," European Journal of Operational Research, Elsevier, vol. 322(1), pages 237-253.
  10. Pyzhov, Vladislav & Pyzhov, Stanislav, 2017. "Comparison of methods of data mining techniques for the predictive accuracy," MPRA Paper 79326, University Library of Munich, Germany.
  11. Ayed Alwadain & Rao Faizan Ali & Amgad Muneer, 2023. "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
  12. John J. Nay & Yevgeniy Vorobeychik, 2016. "Predicting Human Cooperation," Papers 1601.07792, arXiv.org, revised Apr 2016.
  13. Shusaku Tsumoto & Tomohirno Kimura & Shoji Hirano, 2021. "Determination of Disease from Discharge Summaries," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 49-66, June.
  14. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
  15. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
  16. Airola, Antti & Pahikkala, Tapio & Waegeman, Willem & De Baets, Bernard & Salakoski, Tapio, 2011. "An experimental comparison of cross-validation techniques for estimating the area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1828-1844, April.
  17. Qianying Jin & Kristiaan Kerstens & Ignace Van de Woestyne, 2024. "Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1213-1239, December.
  18. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
  19. Mark Lown & Michael Brown & Chloë Brown & Arthur M Yue & Benoy N Shah & Simon J Corbett & George Lewith & Beth Stuart & Michael Moore & Paul Little, 2020. "Machine learning detection of Atrial Fibrillation using wearable technology," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-9, January.
  20. Zhengnan Huang & Hongjiu Zhang & Jonathan Boss & Stephen A Goutman & Bhramar Mukherjee & Ivo D Dinov & Yuanfang Guan & for the Pooled Resource Open-Access ALS Clinical Trials Consortium, 2017. "Complete hazard ranking to analyze right-censored data: An ALS survival study," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-21, December.
  21. Junying Wang & David D Wu & Christine DeLorenzo & Jie Yang, 2024. "Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-26, March.
  22. Shusaku Tsumoto & Tomohiro Kimura & Shoji Hirano, 2022. "Expectation–Maximization (EM) Clustering as a Preprocessing Method for Clinical Pathway Mining," The Review of Socionetwork Strategies, Springer, vol. 16(1), pages 25-52, April.
  23. Borra, Simone & Di Ciaccio, Agostino, 2010. "Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2976-2989, December.
  24. Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.
  25. Sumeyra Sahbaz & Kazim Topuz & Seth J. Schwartz & Pablo Montero-Zamora, 2025. "Understanding cultural stress and mental health among Latinos in the us: probabilistic omnidirectional inference model," Annals of Operations Research, Springer, vol. 346(3), pages 2461-2481, March.
  26. Zhijian Wang & Likang Zheng & Junyuan Wang & Wenhua Du, 2019. "Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine," Complexity, Hindawi, vol. 2019, pages 1-19, November.
  27. Conde, David & Fernández, Miguel & Salvador, Bonifacio & Rueda, Cristina, 2015. "dawai: An R Package for Discriminant Analysis with Additional Information," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i10).
  28. Mark G E White & Neil E Bezodis & Jonathon Neville & Huw Summers & Paul Rees, 2022. "Determining jumping performance from a single body-worn accelerometer using machine learning," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
  29. Uri Goldsztejn & Arye Nehorai, 2023. "Predicting preterm births from electrohysterogram recordings via deep learning," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-20, May.
  30. Zachary K. Collier & Haobai Zhang & Bridgette Johnson, 2021. "Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches," Evaluation Review, , vol. 45(6), pages 309-333, December.
  31. Timothy R Brick & Rachel E Koffer & Denis Gerstorf & Nilam Ram, 2018. "Feature Selection Methods for Optimal Design of Studies for Developmental Inquiry," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 73(1), pages 113-123.
  32. Xue, Jing-Hao & Titterington, D. Michael, 2010. "On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 438-451, February.
  33. David Rios Insua & Roi Naveiro & Victor Gallego, 2020. "Perspectives on Adversarial Classification," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
  34. Hosseini, Fatemeh & Eidsvik, Jo & Mohammadzadeh, Mohsen, 2011. "Approximate Bayesian inference in spatial GLMM with skew normal latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1791-1806, April.
  35. Erfan Mahmoodi & Mahmood Azari & Mohammad Taghi Dastorani & Aryan Salvati, 2024. "Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5343-5363, October.
  36. Charlotte Soneson & Sarah Gerster & Mauro Delorenzi, 2014. "Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
  37. Richard A. Johansen & Molly K. Reif & Christina L. Saltus & Kaytee L. Pokrzywinski, 2024. "A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
  38. Goedhart, Jeroen M. & Klausch, Thomas & van de Wiel, Mark A., 2023. "Estimation of predictive performance in high-dimensional data settings using learning curves," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
  39. Ardia, David & Bluteau, Keven, 2026. "Optimal text-based time-series indices," International Journal of Forecasting, Elsevier, vol. 42(1), pages 44-60.
  40. Gonzalo Perez-de-la-Cruz & Guillermina Eslava-Gomez, 2019. "Discriminant analysis for discrete variables derived from a tree-structured graphical model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 855-876, December.
  41. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.
  42. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
  43. Vinícius Serafini Roglio & Eduardo Nunes Borges & Francisco Diego Rabelo-da-Ponte & Felipe Ornell & Juliana Nichterwitz Scherer & Jaqueline Bohrer Schuch & Ives Cavalcante Passos & Breno Sanvicente-Vi, 2020. "Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.
  44. Dietrichson, Jens & Klokker, Rasmus H., 2024. "Predicting preschool problems," Children and Youth Services Review, Elsevier, vol. 161(C).
  45. Conde David & Salvador Bonifacio & Rueda Cristina & Fernández Miguel A., 2013. "Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(5), pages 583-602, October.
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