Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis
Department of Electrical and Computer Engineering, University of Victoria, EOW 448, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada
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Mach. Learn. Knowl. Extr. 2019, 1(4), 1084-1099; https://doi.org/10.3390/make1040061
Received: 25 September 2019 / Revised: 30 October 2019 / Accepted: 1 November 2019 / Published: 5 November 2019
In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multiple gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance-partial least squares discriminant analysis when the signal-to-noise ratio and training sample size are sufficient.
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Keywords:
multi-label classification; infrared absorption spectroscopy; supervised learning; feedforward neural networks; binary relevance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Gan, L.; Yuen, B.; Lu, T. Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis. Mach. Learn. Knowl. Extr. 2019, 1, 1084-1099. https://doi.org/10.3390/make1040061
AMA Style
Gan L, Yuen B, Lu T. Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis. Machine Learning and Knowledge Extraction. 2019; 1(4):1084-1099. https://doi.org/10.3390/make1040061
Chicago/Turabian StyleGan, Luyun; Yuen, Brosnan; Lu, Tao. 2019. "Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis" Mach. Learn. Knowl. Extr. 1, no. 4: 1084-1099. https://doi.org/10.3390/make1040061
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