Machine learning techniques applied to US army and navy data

Jong Min Kim, Chuwen Li, Il Do Ha

    Research output: Contribution to journalArticlepeer-review

    1 Scopus citations

    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.

    Original languageEnglish (US)
    Pages (from-to)149-166
    Number of pages18
    JournalInternational Journal of Productivity and Quality Management
    Volume29
    Issue number2
    DOIs
    StatePublished - 2020

    Bibliographical note

    Funding Information:
    This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2017R1E1A1A03070747).

    Keywords

    • ANN
    • Artificial neural networks
    • Binary response data
    • Elastic net
    • Lasso
    • Ridge

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