Abstract

Abstract. The minimum bounding sphere of a set of data, defined as the smallest sphere enclosing the data, was first used by Schölkopf et al. to estimate the VC-dimension of a classifier and later extended by Tax and Duin for data domain description. Given a set of data, the minimum bounding sphere of each class can be computed by solving a quadratic programming problem. Since the spheres are constructed for each class separately, they can be used to deal with the multi-class classification problem easily, as proposed by Zhu et al. In this paper, we show that the decision rule proposed by Zhu et al. is generally insufficient to achieve the state-of-the-art classification performance. We therefore propose a new decision rule based on the Bayes rule. This new decision rule significantly improves the performance of the resulting classifier. In addition to its low computational complexity and easy expandability, our experimental results show that the new classifier outperforms the standard support vector machine on most of the real-world datasets being tested. 1

Citations