A Proximal Classification Method based on Two Smallest and Supervised Hyperspheres

We propose a proximal classification method, named as the hyperspherical 2-surface proximal (H2SP) classifier, by seeking the two smallest hyperspheres for the positive class and the negative class, respectively, each containing the most samples from one class while also the least samples from the other. The proposed H2SP classifier is validated using five public benchmark datasets, including one toy dataset and four real datasets. The results are compared with those obtained by using Fisher´s linear discriminant analysis (FLDA), support vector machines (SVM), and radial basis function (RBF) networks. Experimental results comparing classification error rates demonstrate the effectiveness of the proposed method.