Abstract
The existing multi-class classification algorithms based on support vector machine (SVM) generally decompose the original problem into smaller subproblems. However, the decomposition approach raises the problems of unreliable and unbalanced training of individual classifiers. The aim of this work is to alleviate these problems. In this paper, a novel multi-class classification algorithm based on one-class SVM is presented. The distinguishing point of our proposed algorithm is that the algorithm solves a one-class classification problem rather than decomposing the problem into several smaller subproblems. The proposed algorithm solves a multi-class classification problem by expanding the training input vector with the class label and constructing a one-class SVM with the expanded input vectors. In the test phase, test input vectors are assessed by fit to the decision boundary under the assumption of belonging to a class. We conducted experiments on several real-world benchmark datasets. Experimental results showed that the proposed algorithm outperforms other SVM-based multi-class classification algorithms.