Title: Machine learning techniques applied to US army and navy data

Authors: Jong-Min Kim; Chuwen Li; Il Do Ha

Addresses: Division of Science and Mathematics, University of Minnesota-Morris, Morris, Minnesota, USA ' Division of Science and Mathematics, University of Minnesota-Morris, Morris, Minnesota, USA ' Department of Statistics, Pukyong National University, Busan, South Korea

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.

Keywords: binary response data; artificial neural networks; ANN; ridge; lasso; elastic net.

DOI: 10.1504/IJPQM.2020.105976

International Journal of Productivity and Quality Management, 2020 Vol.29 No.2, pp.149 - 166

Received: 27 Aug 2018
Accepted: 10 Nov 2018
Published online: 23 Mar 2020 *

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