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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. View Full-Text
Keywords: multi-label classification; infrared absorption spectroscopy; supervised learning; feedforward neural networks; binary relevance multi-label classification; infrared absorption spectroscopy; supervised learning; feedforward neural networks; binary relevance
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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.

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