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.
Highlights
Spectroscopic analysis sees multiple applications in physics, chemistry, bioinformatics, geophysics, astronomy, etc. It has been widely used for detecting mineral samples [1], gas emission [2] and food volatiles [3]. Multivariate regression algorithms such as principal component regression [4] and partial least squares (PLS) [5] are fundamental and popular tools that have been successfully applied to spectroscopic analysis
In the feedforward neural networks (FNN)-optimal thresholding (OT) model, scores of all labels need to be calculated for ranking purposes, and a threshold decision model will be employed to assign a set of labels to the sample in the label prediction step
In both training and testing, the 1000-point absorbance spectra will be pre-processed with principal component analysis (PCA), and the principal components will be the input of the FNN model (x)
Summary
Spectroscopic analysis sees multiple applications in physics, chemistry, bioinformatics, geophysics, astronomy, etc. Non-linear methods, such as support vector machine [6], genetic programming [7] and artificial neural networks (ANN) [1], are adopted to increase prediction accuracy These algorithms focus on either regression or single-label classification problems. The restricted Boltzmann machine [27], feedforward neural networks (FNN) [28,29], convolutional neural networks (CNN) [30,31], and recurrent neural networks (RNN) [32] are employed to characterize label dependency in image processing or to find feature representations in text classification Those adaptive methods can identify multiple labels simultaneously and efficiently without being repeatedly trained for sets of labels or chains of classifiers. It will be shown that for most evaluation metrics, the adaptive FNN model has the best performance
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