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Cited by 81Imbalanced Data Classification TechniquesMachine Learning and Data ClassificationData Stream Mining Techniques

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The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data

Johnson, Justin M., Khoshgoftaar, Taghi M.·2020·Information Systems Frontiers
DOI 10.1007/s10796-020-10022-7
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  • Machine learning training is severely impaired by imbalanced class sizes.

    “It has been well-documented that machine learning training is severely impaired by imbalanced class sizes (Krawczyk, 2016; Johnson and Khoshgoftaar, 2020).”

    From Deep Learning-Based Extreme Heatwave Forecast (2022) · cites this paper as Johnson and Khoshgoftaar, 2020

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Metadata

Authors
Johnson, J. M., & Khoshgoftaar, T. M.
Affiliations
Florida Atlantic University (US)
Corresponding author
Johnson, Justin M.
ORCID
Johnson, Justin M.
Venue
Information Systems Frontiers
Publisher
Springer Science and Business Media LLC
Published
2020-06-21
Volume
22
Issue
5
Pages
1113-1131
ISSN
  • 1387-3326 (ISSN-L)
  • 1572-9419
DOI
10.1007/s10796-020-10022-7
OpenAlex
W3035999516
Cited by
81
References
3
Citations by year
2026: 8 · 2025: 7 · 2024: 12 · 2023: 12 · 2022: 16 · 2021: 22 · 2020: 4
FWCI
5.22
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Information Systems Frontiers (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
Keywords
class imbalance, data sampling, deep neural network, big data, random oversampling, random undersampling, sample size effects, prediction model performance

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