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arXiv · 1906.10242

Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis

Luyun Gan·Brosnan Yuen·Tao Lu
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Abstract

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi 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 signal-to-noise ratio and training sample size are sufficient.

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BibTeXRIS

Luyun Gan, Brosnan Yuen, Tao Lu. 2019-06-24. Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis. https://arxiv.org/abs/1906.10242

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