Parameter selection of Gaussian kernel for one-class SVM.

One-class classification (OCC) builds models using only the samples from one class (the target class) so as to predict whether a new-coming sample belongs to the target class or not. OCC widely exists in many application fields, such as fault detection. As an effective tool for OCC, one-class SVM (OCSVM) with the Gaussian kernel has received much attention recently. However, its kernel parameter selection greatly affects its performance and is still an open problem. This paper proposes a novel method to solve this problem. First, an effective way is presented to measure the distances from the samples to the OCSVM enclosing surfaces. Then based on this measurement, an optimization objective function for the parameter selection is put forward. Extensive experiments are conducted on various data sets, and the results verify the effectiveness of the proposed method.

Hyperparameter Selection for Gaussian Process One-Class Classification.

Gaussian processes (GPs) provide predicted outputs with a full conditional statistical description, which can be used to establish confidence intervals and to set hyperparameters. This characteristic provides GPs with competitive or better performance in various applications. However, the specificity of one-class classification (OCC) makes GPs unable to select suitable hyperparameters in their traditional way. This brief proposes to select hyperparameters for GP OCC using the prediction difference between edge and interior positive training samples. Experiments on 2-D artificial and University of California benchmark data sets verify the effectiveness of this method.