at Google ScholarTitle data
Kampe, Bernd ; Kloß, Sandra ; Bocklitz, Thomas ; Rösch, Petra ; Popp, Jürgen:
Recursive feature elimination in Raman spectra with support vector machines.
In: Frontiers of Optoelectronics.
Vol. 10
(2017)
.
- pp. 273-279.
ISSN 2095-2767
DOI: https://doi.org/10.1007/s12200-017-0726-4
Abstract in another language
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.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 16 May 2023 12:16 |
| Last Modified: | 16 May 2023 12:16 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76332 |