Recursive feature elimination in Raman spectra with support vector machines

作     者：Bernd KAMPE Sandra KLOβ Thomas BOCKLITZ Petra ROSCH Jurgen POPP 

作者机构：Institute of Physical Chemistry and Abbe Center of Photonics University of Jena Helmholtzweg 4 D-07743 Jena Germany InfectoGnostics Research Campus Jena Center for Applied Research Philosophenweg 7 07743 Jena Germany Leibniz-Institute of Photonic Technology Albert-Einstein-Straβe 9 D-07745 Jena Germany 

出 版 物：《Frontiers of Optoelectronics》 (光电子前沿（英文版）)

年 卷 期：2017年第10卷第3期

页      面：273-279页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081704[工学-应用化学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0817[工学-化学工程与技术] 070302[理学-分析化学] 0835[工学-软件工程] 0703[理学-化学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funding of the research project InterSept from the Federal Ministry of Education and Research  Germany (BMBF) is gratefully acknowledged 

主　　题：feature selection Raman spectroscopy pat-tern recognition chemometrics 

摘      要：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.