In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to asubstantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

中文翻译：

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一种多标签分类的惰性特征选择方法

在许多重要的应用领域中，例如文本分类，生物分子分析，场景或视频分类和医学诊断，实例自然与多个类别标签相关联，从而引发了多标签分类问题。近年来，这导致了对多标签分类的大量研究。更具体地，已经开发了特征选择方法以允许识别用于多标签分类的相关和信息特征。这项工作提出了一种基于惰性特征选择范式并且特定于多标签上下文的新特征选择方法。实验结果表明，与文献中当前使用的多标签特征选择技术相比，该技术具有竞争优势，并且显然更具可扩展性，