期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：10
页码：201-210
出版社：SERSC
摘要：Rapid and timely monitoring of traumatic inflammation is conducive to doctors’ diagnosis and treatment.It has been proved that electronic nose (E-nose) is an effective way to predict the bacterial class of wound infection by smelling the odor produced by the metabolites, and the classification accuracy of E-nose is influenced strongly by the classifier. To improve the performance of E-nose in predicting the bacterial class of wound infection, an enhanced SVM with a novel weighted Gaussian RBF kernel is proposed in this paper, and the way of setting parameters of this enhanced SVM is also given. Experimental results prove that the classification accuracy of SVM with the novel weighted Gaussian RBF kernel is 95.24%, which is better than other considered classifiers (PLS-DA, RBFNN, SVM with single Gaussian RBF kernel and SVM with traditional weighted Gaussian RBF kernel). All results make it clear that the enhanced SVM proposed in this paperis an ideal classifier when E-nose is used to detect the bacterial class of wound infection.
