School of Computer Science, National University of Ireland, Galway H91 TK33, Ireland.
J Chem Inf Model. 2020 Apr 27;60(4):1936-1954. doi: 10.1021/acs.jcim.9b01037. Epub 2020 Mar 16.
This paper presents a new approach to classification of high-dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches. The specific task we consider is identifying whether samples contain chlorinated solvents or not, based on their Raman spectra. We also examine robustness to classification of outlier samples that are not represented in the training set (negative outliers). A novel application of a locally connected neural network (NN) for the binary classification of spectroscopy data is proposed and demonstrated to yield improved accuracy over traditionally popular algorithms. Additionally, we present the ability to further increase the accuracy of the locally connected NN algorithm through the use of synthetic training spectra, and we investigate the use of autoencoder based one-class classifiers and outlier detectors. Finally, a two-step classification process is presented as an alternative to the binary and one-class classification paradigms. This process combines the locally connected NN classifier, the use of synthetic training data, and an autoencoder based outlier detector to produce a model which is shown to both produce high classification accuracy and be robust in the presence of negative outliers.
本文提出了一种新的高维光谱数据分析分类方法，并证明其优于其他当前最先进的方法。我们考虑的具体任务是根据拉曼光谱识别样本是否含有氯化溶剂。我们还检查了对未在训练集中表示的异常样本（负异常值）分类的稳健性。本文提出了一种局部连接神经网络（NN）在光谱数据分析中的二进制分类中的新应用，并证明其在准确性方面优于传统的流行算法。此外，我们提出了通过使用合成训练光谱进一步提高局部连接神经网络算法准确性的能力，并研究了基于自动编码器的单类分类器和异常值检测器的使用。最后，提出了一种两步分类过程作为二进制和单类分类范例的替代方法。该过程结合了局部连接神经网络分类器、合成训练数据的使用和基于自动编码器的异常值检测器，以生成一个模型，该模型被证明在存在负异常值的情况下既能产生高分类准确性又具有稳健性。
