An extended one-versus-rest support vector machine for multi-label classification

作者：Xu, Jianhua^*

来源：Neurocomputing, 2011, 74(17): 3114-3124.

DOI：10.1016/j.neucom.2011.04.024

• Support vector machine
• Multi-label classification
• Data decomposition
• Algorithm extension
• Hybrid strategy
• One-versus-rest

Hybrid strategy, which generalizes a specific single-label algorithm while one or two data decomposition tricks are applied implicitly or explicitly, has become an effective and efficient tool to design and implement various multi-label classification algorithms. In this paper, we extend traditional binary support vector machine by introducing an approximate ranking loss as its empirical loss term to build a novel support vector machine for multi-label classification, resulting into a quadratic programming problem with different upper bounds of variables to characterize label correlation of individual instance. Further, our optimization problem can be solved via combining one-versus-rest data decomposition trick with modified binary support vector machine, which dramatically reduces computational cost. Experimental study on ten multi-label data sets illustrates that our method is a powerful candidate for multi-label classification, compared with four state-of-the-art multi-label classification approaches.

• 出版日期2011-10
• 单位南京师范大学