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Multi-label classification problem is a special learning task in which its classes are not mutually exclusive and each sample may belong to several classes simultaneously. A novel multi-label classification algorithm based on both one-versus-one decomposition method and triple class support vector machine (SVM) is presented in this paper. One-versus-one decomposition technique is used to pairwise divide a multi-label classification problem into many binary class ones, in which some samples possibly are associated with two labels at the same time. Triple class SVM is a generalization of traditional binary class SVM, where those samples with double labels are considered as a mixed class located between positive and negative classes. Experimental results on benchmark datasets Yeast and Scene demonstrate that our proposed algorithm is comparable with some existed methods, such as rank-SVM, binary-SVM, ML-kNN and etc, according to several evaluation criteria of multi-label learning algorithms.