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Recursive feature elimination in Raman spectra with support vector machines
Ist Teil von
Frontiers of Optoelectronics (Online), 2017-09, Vol.10 (3), p.273-279
Ort / Verlag
Beijing: Higher Education Press
Erscheinungsjahr
2017
Link zum Volltext
Quelle
SpringerLink
Beschreibungen/Notizen
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.