Machine learning techniques applied to US army and navy data
by Jong-Min Kim; Chuwen Li; Il Do Ha
International Journal of Productivity and Quality Management (IJPQM), Vol. 29, No. 2, 2020

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.

Online publication date: Mon, 23-Mar-2020

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