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SPlit: An Optimal Method for Data Splitting

arXiv 2012.10945 ↗First posted 20 Dec 2020stat.ML cs.LGcited by 15

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Abstract (arXiv)

In this article we propose an optimal method referred to as SPlit for splitting a dataset into training and testing sets. SPlit is based on the method of Support Points (SP), which was initially developed for finding the optimal representative points of a continuous distribution. We adapt SP for subsampling from a dataset using a sequential nearest neighbor algorithm. We also extend SP to deal with categorical variables so that SPlit can be applied to both regression and classification problems. The implementation of SPlit on real datasets shows substantial improvement in the worst-case testing performance for several modeling methods compared to the commonly used random splitting procedure.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Dec 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 2; 2024: 4; 2025: 2; 2026: 2.

15 papers.

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  1. 2022Optimal Ratio for Data Splittingcited by 17
  2. 2021Data Twinningcited by 10
  3. 2022A Continual Learning Framework for Adaptive Defect Classification and Inspectioncited by 7
  4. 2023A Global-Local Approximation Framework for Large-Scale Gaussian Process Modelingcited by 2
  5. 2023Rational Krigingcited by 1
  6. 2021JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Predictioncited by 0
  7. 2021Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameterscited by 0
  8. 2024A model-free subdata selection method for classificationcited by 0
  9. 2024Using a CNN Model to Assess Paintings' Creativitycited by 0
  10. 2024Benchmarking of a new data splitting method on volcanic eruption datacited by 0
  11. 2024Double Machine Learning for Adaptive Causal Representation in High-Dimensional Datacited by 0
  12. 2025Personalized Control for Lower Limb Prosthesis Using Kolmogorov-Arnold Networkscited by 0
3 more
  1. 2025Harnessing Near-Infrared Spectroscopy and Machine Learning for Traceable Classification of Hanwoo and Holstein Beefcited by 0
  2. 2026Nearly Optimal Subdata Selectioncited by 0
  3. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0