MLTSVM: A novel twin support vector machine to multi-label learning
MLTSVM: A novel twin support vector machine to multi-label learning
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MLTSVM：一种新颖的多标签学习双支持向量机
DOI：
10.1016/j.patcog.2015.10.008
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发表时间：
2016-04-01
影响因子：
8
通讯作者：
Deng, Nai-Yang
中科院分区：
文献类型：
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作者：
Chen, Wei-Jie;Shao, Yuan -Hai;Deng, Nai-Yang
多标签学习范式是一种针对潜在多标签数据的学习方法，近年来受到机器智能领域的广泛关注。在本文中，我们提出了一种新的多标签双支持向量机（MLTSVM）的多标签分类。MLTSVM确定多个非平行超平面来捕获数据中嵌入的多标签信息，这是对双支持向量机（TWSVM）多标签分类的有益推广。为了加快训练过程，一个有效的逐次超松弛（SOR）算法被开发用于解决MLTSVM中涉及的二次规划问题（QPP）。在人工和真实多标签数据集上的大量实验结果证实了所提出的MLTSVM的可行性和有效性。(C)2015爱思唯尔有限公司版权所有。
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. (C) 2015 Elsevier Ltd. All rights reserved.