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.

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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