Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection

As one of the methods to solve one-class classification problems (OCC), one-class support vector machines (OCSVM) have been applied to fault detection in recent years. Among all the kernels available for OCSVM, the Gaussian kernel is the most commonly used one. The selection of Gaussian kernel parameters influences greatly the performances of classifiers, which remains as an open problem. In this paper two methods are proposed to select Gaussian kernel parameters in OCSVM: according to the first one, the parameters are selected using the information of the farthest and the nearest neighbors of each sample; using the second one, the parameters are determined via detecting the “tightness” of the decision boundaries. The two proposed methods are tested on UCI data sets and Tennessee Eastman Process benchmark data sets. The results show that, the two proposed methods can be used to select suitable parameters for the Gaussian kernel, enabling the resulting OCSVM models to perform well on fault detection.