APPLYING DISCRIMINANT FUNCTIONS WITH ONE-CLASS SVMS FOR MULTI-CLASS CLASSIFICATION

摘要

Early SVM-based multi-class classification algorithms work by splitting the original problem into a set of two-class sub-problems.The time and space required by these algorithms are very much demanding.We present in this paper a hybrid method that integrates several one-class SVMs with discriminant functions to solve the multi-class classification problem.Several discriminant functions, including similarity measure, distance measure, and Z-score measure, have been applied in this research.The proposed method has low time and space complexities.Experimental results show that our method compares favorably with SVDD-based multi-class classification algorithms on several real datasets from LCI and Statlog.