Mining Multi-label Data
多标签分类学习,This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Section 3 discusses dimensionality reduction methods for multi-label data. Sections 4 and 5 discuss two important research challenges, which, if successfully met, can signif- icantly expand the real-world applications of multi-label learning methods: a) ex- ploiting label structure and b) scaling up to domains with large number of labels. Section 6 introduces benchmark multi-label datasets and their statistics, while Sec- tion 7 presents the most frequently used evaluation measures for multi-label learn-
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