An Approach to Choosing Gaussian Kernel Parameter for One-Class SVMs via Tightness Detecting

In recent years, one-class support vector machines (OCSVMs) have received increasing attention, which are one of the methods to solve one-class classification problems. Among all the kernels available to OCSVMs, Gaussian kernel is the most commonly used one with a single parameter S to tune, which influences classifier performance significantly. This paper proposes a novel heuristic approach to choosing this parameter via tightness detecting, that is designed to detect whether the decision boundaries are satisfactory. The approach tunes the parameter to ensure that the decision boundaries have an appropriate tightness, only according to the geometric distribution of positive samples. Experimental results on different datasets show that the proposed approach has a better performance than previous methods.