Multi-label classification methods aim to a class of application problems where each individual contains a single instance while associates with a set of labels simultaneously. In this paper, we formulate a novel multi-label classification method based on extreme learning machine framework, named ELM-MC algorithm. The essence of ELM-MC algorithm is to convert the multi-label classification problem into some single-label classifications, and fully considers the relationship among different labels. After the classification of one label, the associations with next label are applied to update the learning parameters in ELM-MC algorithm. In addition, we design a backup pool for the hidden nodes. It can help to select relatively suitable hidden nodes to the corresponding label classification case. In the simulation part, six famous databases are applied to demonstrate the satisfied classification accuracy of the proposed method.

中文翻译：

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ELM-MC：基于极限学习机的多标签分类框架

多标签分类方法针对一类应用程序问题，其中每个人都包含一个实例，同时与一组标签关联。在本文中，我们提出了一种基于极限学习机框架的新型多标签分类方法，称为ELM-MC算法。ELM-MC算法的本质是将多标签分类问题转化为一些单标签分类，并充分考虑不同标签之间的关系。在对一个标签进行分类之后，将与下一个标签的关联应用于更新ELM-MC算法中的学习参数。此外，我们为隐藏节点设计了一个备份池。它可以帮助为相应的标签分类案例选择相对合适的隐藏节点。在模拟部分，