Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 05, 2019
- Source ID
- 10.3390/make1040061
Entities
People
- Brosnan Yuen
- Luyun Gan
- Tao Lu
Organizations
- Defense Threat Reduction Agency
- Natural Sciences and Engineering Research Council
- Nvidia