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Machine learning techniques applied to US army and navy data

Author

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  • Jong-Min Kim
  • Chuwen Li
  • Il Do Ha

Abstract

We apply machine learning techniques to the synthetic data (Stevens and Anderson-Cook, 2017a), which is univariate data with a binary response of passing or failing for complex munitions generated to match age and usage rate, found in US Department of Defense complex systems (the army and navy). We propose applying machine learning techniques to predict the binary response of passing or failing for the army and navy data.

Suggested Citation

  • Jong-Min Kim & Chuwen Li & Il Do Ha, 2020. "Machine learning techniques applied to US army and navy data," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 29(2), pages 149-166.
    • Handle: RePEc:ids:ijpqma:v:29:y:2020:i:2:p:149-166
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    Cited by:

    1. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.

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