Adapting radial basis function neural networks for one-class classification

One-class classification (OCC) is to describe one class of objects, called target objects, and discriminate them from all other possible patterns. In this paper, we propose to adapt radial basis function neural networks (RBFNNs) for OCC. First, target objects are mapped into a feature space by using neurons in the hidden layer of the RBFNNs. Then, we perform support vector domain description (SVDD) with linear kernel functions in the feature space to realize OCC. In addition, we also model, in the feature space, the closed sphere centered on the mean of target objects for OCC. Compared to the SVDD with nonlinear kernel functions, our methods can use flexible nonlinear mappings, which do not necessarily satisfy Mercer's conditions. Moreover, we can also control the complexity of solutions easily by setting the number of neurons in the hidden layer of RBFNNs. Experimental results show that the classification accuracies of our methods can be close to, and even can reach those of the SVDD for most of results, but with typically much sparser models.