A new multi-class classification method based on minimum enclosing balls

With respect to classification problems, the Minimum enclosing ball (MEB) method was recently studied by some scholars as a new support vector machine. As a nascent technology, however, MEB reports poor adaptability for different types of samples, especially multi-class samples. In this paper, we propose a new multi-class classification method based on MEB. This method is derived from each class sample center and radius with the Gaussian kernel width factor parameter sigma, which is labelled as sigma-MEB. sigma is a variable parameter according to the different sample characteristics. When this parameter is considered, the multi-class classifier is easy to adapt and is robust in diverse datasets. The quadratic programming problem was transformed into its dual form with Lagrange multipliers using this method. Finally, we applied sequential minimal optimization method and Karush-Kuhn-Tucker conditions to accelerate the training process. Numerical experiment results indicate that for given different types of samples, the proposed method is more accurate than the methods with which it is compared. Moreover, the proposed method reports values in the upper quantile with respect to adaptation performance.