Constructing a multi-class classifier using one-against-one approach with different binary classifiers

For the one-against-one approach, all the binary classifiers that form a one-against-one classifier should be sufficiently competent. If some of the classifiers are not competent, the consequences might be invalid classification results. To address the problem, we propose diversified one-against-one (DOAO) method that seeks to find the best classification algorithm for each class pair when applying the one-against-one approach to multi-class classification problems. Applying the proposed method makes various classification algorithms to complement each other. Since the best classification algorithm for each class pair is different, the proposed method can obtain improved classification results. Experimental results show that the proposed method outperforms other one-against-one based methods.