MLTSVM: A novel twin support vector machine to multi-label learning. (April 2016) - Chen, Wei-Jie; Shao, Yuan-Hai; Li, Chun-Na; Deng, Nai-Yang
Abstract: Multi-label learning paradigm, which aims at dealing with data associated with potential multiple labels, has attracted a great deal of attention in machine intelligent community. In this paper, we propose a novel multi-label twin support vector machine (MLTSVM) for multi-label classification. MLTSVM determines multiple nonparallel hyperplanes to capture the multi-label information embedded in data, which is a useful promotion of twin support vector machine (TWSVM) for multi-label classification. To speed up the training procedure, an efficient successive overrelaxation (SOR) algorithm is developed for solving the involved quadratic programming problems (QPPs) in MLTSVM. Extensive experimental results on both synthetic and real-world multi-label datasets confirm the feasibility and effectiveness of the proposed MLTSVM. Abstract : Highlights: The first nonparallel hyperplane SVM (MLTSVM) classifier applied in multi-label learning is proposed. The multi-label information of the dataset can be effectively captured by multiple nonparallel hyperplanes. The ambiguity of the predicting procedure is avoided by the effective decision function. An efficient SOR algorithm is applied to solve the proposed MLTSVM. Experimental results confirm the feasibility and superiority of the proposed MLTSVM.