Abstract
We proposed a kernel-based binary classification algorithm, named support vector class description (SVCD), which is an extended version of support vector domain description (SVDD) for one-class classification. SVCD constructs two compact hyperspheres in the feature space such that each hypersphere includes as many instances as possible of one class, while keeping the instances of the other class away from the sphere. By doing this, two linearly non-separable classes in the input space can be well distinguished in the feature space. In order to verify the classification performance and exploit the properties of SVCD, we conducted experiments on actual classification data sets and analyzed the results. Compared with other popular kernel-based classification algorithms, such as support vector machine (SVM) and kernel Fisher discriminant analysis (KFD), SVCD gave better classification performances in terms of both the area under the receiving operator curve (AUROC) and the balanced correction rate (BCR). In addition, SVCD was found to be capable of finding moderate sparse solutions with little parameter sensitivity.