A Fast Multi-label Classification Algorithm Based on Double Label Support Vector Machine
A Fast Multi-label Classification Algorithm Based on Double Label Support Vector Machine
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DOI：
10.1109/cis.2009.168
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发表时间：
2009-12
期刊：
影响因子：
--
通讯作者：
Jiayang Li;Jianhua Xu
中科院分区：
文献类型：
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作者：
Jiayang Li;Jianhua Xu
将一对一分解策略与支持向量机相结合已成为解决多标签分类问题的有效手段。但如何加快其训练和测试程序仍然是一个具有挑战性的问题。在本文中，我们将初级二元支持向量机推广到通过将双标签实例定位在正例和负例之间的边缘区域来构造双标签支持向量机，然后利用投票规则设计快速多标签分类算法。在基准数据集 Yeast 和 Scene 上的实验表明，根据一些广泛使用的评估标准，我们的新方法可以与一些现有方法相媲美，并且在训练过程中比当前相应方法平均运行速度快 17%。
Combing one-versus-one decomposition strategy with support vector machine has become an efficient means for multi-label classification problem. But how to speed up its training and test procedures is still a challenging issue. In this paper, we generalize the primary binary support vector machine to construct a double label support vector machine through locating double label instances at marginal region between positive and negative instances, and then design a fast multi-label classification algorithm using the voting rule. Experiments on benchmark datasets Yeast and Scene illustrate that our novel method can be comparable with some existing methods according to some widely used evaluation criteria, and can run faster 17% averagely than the current corresponding method in training procedure.