Gan, LuyunYuen, BrosnanLu, Tao2019-12-182019-12-1820192019Gan, L. & Yuen, B. & Lu, T. (2019). Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis. Machine Learning & Knowledge Extraction, 1(4), 1084-1099. https://doi.org/10.3390/make1040061https://doi.org/10.3390/make1040061http://hdl.handle.net/1828/11389In 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.enmulti-label classificationinfrared absorption spectroscopysupervised learningfeedforward neural networksbinary relevanceArticleDepartment of Electrical and Computer Engineering