A twin-hyperspheres support vector machine with automatic variable weights for data classification

This paper proposes a novel twin-hyperspheres support vector machine (THSVM) classifier for binary classification, called the automatic variable-weighted THSVM (VTHSVM) classifier. By solving a single optimization problem, this classifier not only finds a pair of hyperspheres for classification, but also automatically constructs a weight vector for each class in order to describe the dissimilarity of different classes. This VTHSVM is extended to the kernel case by the fact that a kernel can be written as a sum of one's evaluated on each variable separately. The main advantage of this method is that it allows the use of adaptive distance, which is suitable to find an as compact as possible hypersphere for each class. Experiments with synthetic and benchmark datasets indicate VTHSVM obtains better performance than some other classifiers.