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

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  • Justin M. Johnson

    (Florida Atlantic University)

  • Taghi M. Khoshgoftaar

    (Florida Atlantic University)

Abstract

Training predictive models with class-imbalanced data has proven to be a difficult task. This problem is well studied, but the era of big data is producing more extreme levels of imbalance that are increasingly difficult to model. We use three data sets of varying complexity to evaluate data sampling strategies for treating high class imbalance with deep neural networks and big data. Sampling rates are varied to create training distributions with positive class sizes from 0.025%–90%. The area under the receiver operating characteristics curve is used to compare performance, and thresholding is used to maximize class performance. Random over-sampling (ROS) consistently outperforms under-sampling (RUS) and baseline methods. The majority class proves susceptible to misrepresentation when using RUS, and results suggest that each data set is uniquely sensitive to imbalance and sample size. The hybrid ROS-RUS maximizes performance and efficiency, and is our preferred method for treating high imbalance within big data problems.

Suggested Citation

  • Justin M. Johnson & Taghi M. Khoshgoftaar, 2020. "The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data," Information Systems Frontiers, Springer, vol. 22(5), pages 1113-1131, October.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10022-7
    DOI: 10.1007/s10796-020-10022-7
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    References listed on IDEAS

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    1. José I. Requeno & José Merseguer & Simona Bernardi & Diego Perez-Palacin & Giorgos Giotis & Vasilis Papanikolaou, 2019. "Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study," Information Systems Frontiers, Springer, vol. 21(1), pages 67-85, February.
    2. Taghi M. Khoshgoftaar & Kehan Gao & Amri Napolitano & Randall Wald, 2014. "A comparative study of iterative and non-iterative feature selection techniques for software defect prediction," Information Systems Frontiers, Springer, vol. 16(5), pages 801-822, November.
    3. Atreyi Kankanhalli & Jungpil Hahn & Sharon Tan & Gordon Gao, 2016. "Big data and analytics in healthcare: Introduction to the special section," Information Systems Frontiers, Springer, vol. 18(2), pages 233-235, April.
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    Cited by:

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