Recursive feature elimination in Raman spectra with support vector machines

doi:10.1007/s12200-017-0726-4

Front. Optoelectron.

2017, Vol. 10

Issue (3) : 273-279 https://doi.org/10.1007/s12200-017-0726-4

RESEARCH ARTICLE

Recursive feature elimination in Raman spectra with support vector machines

Bernd KAMPE¹, Sandra KLOß^1,², Thomas BOCKLITZ^1,², Petra RÖSCH^1,², Jürgen POPP^1,^2,³(

)

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-Straße 9, D-07745 Jena, Germany

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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.

Keywords feature selection Raman spectroscopy pattern recognition chemometrics

Corresponding Author(s): Jürgen POPP

Just Accepted Date: 21 June 2017 Online First Date: 14 July 2017 Issue Date: 26 September 2017

Cite this article:

Bernd KAMPE,Sandra KLOß,Thomas BOCKLITZ, et al. Recursive feature elimination in Raman spectra with support vector machines[J]. Front. Optoelectron., 2017, 10(3): 273-279.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-017-0726-4
https://academic.hep.com.cn/foe/EN/Y2017/V10/I3/273

Tab.1 Numbers of spectra per data set

Fig.1 Sample preprocessed Raman spectrum of E. faecalis showing the induced ranking of the method

Fig.2 Development of the cross-validation error rate on the training data set over the course of feature removal

Fig.3 Development of the classification error rate on the independent validation data set over the course of feature removal

Fig.4 Development of the classification error rate on the data set of patient samples over the course of feature removal

Fig.5 Trend of the accuracy of the model discriminating E. faecalis from E. coli based on cross-validation

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