Gaussian Kernel Parameter Optimization in One-Class Support Vector Machines

The one-class support vector machines with Gaussian kernel function is a promising machine learning method which have been employed extensively in the area of anomaly detection. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter σ. This paper proposes a new algorithm named Edged Support Vector (ESV) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the selected support vector samples. The algorithm selects the optimal value of σ which leads to a decision boundary that has all its support vectors reside on the surface of the training data (i.e. edged support vector). A support vector is identified as an edge sample by constructing a hyperplane with its k-nearest neighbour samples using a hard margin linear support vector machine. The algorithm was successfully validated using two real world sensing datasets, one collected from a lab specimen which was replicated a jack arch from the Sydney Harbour Bridge, and another one collected from sensors mounted on vehicles for road condition assessment. Results show that the designed ESV algorithm is an appropriate choice to identify the optimal value of σ for OCSVM.