Recursive feature elimination in Raman spectra with support vector machines
Kampe B, Kloß S, Bocklitz T, Rösch P, Popp J (2017)
Publication Type: Journal article
Publication year: 2017
Journal
Book Volume: 10
Pages Range: 273-279
Journal Issue: 3
DOI: 10.1007/s12200-017-0726-4
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.
Involved external institutions
Germany (DE)How to cite
APA:
Kampe, B., Kloß, S., Bocklitz, T., Rösch, P., & Popp, J. (2017). Recursive feature elimination in Raman spectra with support vector machines. Frontiers of Optoelectronics, 10(3), 273-279. https://doi.org/10.1007/s12200-017-0726-4
MLA:
Kampe, Bernd, et al. "Recursive feature elimination in Raman spectra with support vector machines." Frontiers of Optoelectronics 10.3 (2017): 273-279.
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