Frontiers of Optoelectronics, 2017, 10 (3): 273, 网络出版: 2018-01-17  

Recursive feature elimination in Raman spectra with support vector machines

Recursive feature elimination in Raman spectra with support vector machines
作者单位
1 Institute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Helmholtzweg 4, D-07743 Jena, Germany
2 InfectoGnostics Research Campus Jena, Center for Applied Research, Philosophenweg 7, 07743 Jena, Germany
3 Leibniz-Institute of Photonic Technology, Albert-Einstein-Stra?e 9, D-07745 Jena, Germany
摘要
Abstract
The presence of irrelevant and correlated data points in a Raman spectrum can lead to a decline in classifier performance. We introduce support vector machine (SVM)-based recursive feature elimination into the field of Raman spectroscopy and demonstrate its performance on a data set of spectra of clinically relevant microorganisms in urine samples, along with patient samples. As the original technique is only suitable for two-class problems, we adapt it to the multi-class setting. It is shown that a large amount of spectral points can be removed without degrading the prediction accuracy of the resulting model notably.
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Bernd KAMPE, Sandra KLOβ, Thomas BOCKLITZ, Petra ROSCH, Jürgen POPP. Recursive feature elimination in Raman spectra with support vector machines[J]. Frontiers of Optoelectronics, 2017, 10(3): 273. Bernd KAMPE, Sandra KLOβ, Thomas BOCKLITZ, Petra RSCH, Jürgen POPP. Recursive feature elimination in Raman spectra with support vector machines[J]. Frontiers of Optoelectronics, 2017, 10(3): 273.

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