کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
533202 870077 2016 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
MLTSVM: A novel twin support vector machine to multi-label learning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
MLTSVM: A novel twin support vector machine to multi-label learning
چکیده انگلیسی


• 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.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 52, April 2016, Pages 61–74
نویسندگان
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