Indirect Gaussian kernel parameter optimization for one-class SVM in fault detection

摘要

One-class SVM (OCSVM) is widely adopted as an effective method for fault detection, and its Gaussian kernel parameter directly influences its fault detection performance. However, the absence of fault samples in the training set makes it difficult to optimize this parameter. To solve this problem, a novel method of Gaussian kernel parameter optimization is proposed in this paper. This method first automatically selects edge and inner samples from the training set, and then optimizes the parameter through adjusting the distribution of the mappings of edge and inner samples in the feature space, so as to facilitate the building of OCSVM models. Moreover, this method needs not to train OCSVM models during the parameter optimization, which can save computational sources. The effectiveness of this proposed method is testified by experiments on 2D data sets and UCI data sets.