Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification
By S. Yadav, Sanyam Shukla. IEEE International Advance Computing Conference, 2016
661 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/8f7239956f56f32247ee4436f69b09bec2a05eef
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In Artificial Intelligence
- Matthias Feurer
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Citations in the corpus
- A Dutch Dataset for Cross-lingual Multilabel Toxicity Detection (2021, background): “A key risk in ensemble training is overfitting (Pourtaheri and Zahiri, 2016), to mitigate this all models have used a stratified k-fold structure (Yadav and Shukla, 2016).”
- MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles (2021, methodology): “If the proposed method can accurately detect intrusions in both cross-validation and holdout validation, there are no underfitting or overfitting issues [69].”
- Towards Further Automation in AutoML (2018, background): “Several existing works compare different evaluation strategies (Kohavi, 1995; Petrak, 2000; Yadav and Shukla, 2016).”