期刊
FRONTIERS OF OPTOELECTRONICS
卷 10, 期 3, 页码 273-279出版社
HIGHER EDUCATION PRESS
DOI: 10.1007/s12200-017-0726-4
关键词
feature selection; Raman spectroscopy; pattern recognition; chemometrics
资金
- Federal Ministry of Education and Research, Germany (BMBF) [13N13852]
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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