Machine learning techniques applied to US army and navy data

Jong Min Kim; Chuwen Li; Il Do Ha

doi:10.1504/IJPQM.2020.105976

Machine learning techniques applied to US army and navy data

Jong Min Kim, Chuwen Li, Il Do Ha

Statistics (Morris)

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2 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 language	English (US)
Pages (from-to)	149-166
Number of pages	18
Journal	International Journal of Productivity and Quality Management
Volume	29
Issue number	2
DOIs	https://doi.org/10.1504/IJPQM.2020.105976
State	Published - 2020

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Publisher Copyright:
Copyright © 2020 Inderscience Enterprises Ltd.

Keywords

ANN
Artificial neural networks
Binary response data
Elastic net
Lasso
Ridge

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author = "Kim, {Jong Min} and Chuwen Li and Ha, {Il Do}",

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AU - Ha, Il Do

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AB - 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.

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KW - Binary response data

KW - Elastic net

KW - Lasso

KW - Ridge

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ER -

Machine learning techniques applied to US army and navy data

Abstract

Bibliographical note

Keywords

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