Cited by 310Machine Learning and Data ClassificationData Mining and Machine Learning ApplicationsImbalanced Data Classification Techniques

Nested cross-validation when selecting classifiers is overzealous for most practical applications

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Wainer, Jacques, Cawley, Gavin·2021·Expert Systems with Applications

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Research works printed but not linked: 4

  1. [4]4. Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford Univer- sity Press.
  2. [10]10. Dasarathy, B.V. (Ed.), 1991. Nearest Neighbour (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press.
  3. [19]19. Hutter, F., Kotthoff, L., Vanschoren, J., 2019. Automatic Machine Learning: Methods, Systems, Challenges. Springer.
  4. [20]20. Kohonen, T., 1995. Learning vector quantization. Springer.

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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 · 2020: 12 · 2019: 2
FWCI
14.93
Type
journal article
Language
en
Open access
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

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