Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis
Machine Learning
2019-06-26 v1 Signal Processing
Machine Learning
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.
Keywords
Cite
@article{arxiv.1906.10242,
title = {Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis},
author = {Luyun Gan and Brosnan Yuen and Tao Lu},
journal= {arXiv preprint arXiv:1906.10242},
year = {2019}
}
Comments
8 pages, 7 figures