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Multi-label optimal margin distribution machine
Machine Learning ( IF 7.5 ) Pub Date : 2019-10-10 , DOI: 10.1007/s10994-019-05837-8
Zhi-Hao Tan , Peng Tan , Yuan Jiang , Zhi-Hua Zhou

Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method.

中文翻译:

多标签最优边距分配机

多标签支持向量机(Rank-SVM)是一种经典有效的多标签分类算法。关键思想是最大化标签对的最小边距,这是从 SVM 扩展而来的。然而,最近的研究表明,最大化最小边距并不一定会带来更好的泛化性能,相反,优化边距分布更为关键。受这个想法的启发,在本文中,我们首先将边际分布引入多标签学习,并提出了多标签最优边际分布机(mlODM),它有效地优化了所有标签对的边际均值和方差。多个多标签评估指标的大量实验表明,mlODM 优于 SVM 风格的多标签方法。而且,
更新日期:2019-10-10
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