| 多标记学习研究综述 |
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| 引用本文： | 余 鹰. 多标记学习研究综述[J]. 计算机工程与应用, 2015, 51(17): 20-27 |
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| 作者姓名： | 余 鹰 |
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| 作者单位： | 华东交通大学 软件学院，南昌 330013 |
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| 摘 要： | 多标记学习考虑一个对象与多个类别标记相关联的情况，是当前国际机器学习领域研究的热点问题之一。多标记学习的研究主要围绕降低特征空间和标记空间的复杂性，提高多标记学习算法的精度而展开。针对这一特点，从多标记分类、标记排序、多标记维度约简和标记相关性分析四个方面，对多标记学习的研究进展进行了归纳与阐述，分析了当前多标记学习存在的问题。最后指出了目前多标记学习若干发展方向，为该领域的进一步研究提供参考。
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| 关 键 词： | 多标记学习 分类 标记相关性 维度约简 |
Survey on multi-label learning |
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| YU Ying. Survey on multi-label learning[J]. Computer Engineering and Applications, 2015, 51(17): 20-27 |
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| Authors: | YU Ying |
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| Affiliation: | School of Software, East China Jiaotong University, Nanchang 330013, China |
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| Abstract: | Multi-label learning, which considers the case of an object related to multiple labels, attracts much attention in recent years. Multi-label learning research aims to improve the performance of multi-label learning algorithms by reducing the complexity of the feature space and the label space. This paper systematically analyses the developments in multi-label learning research from four aspects including multi-label classification, label ranking, multi-label dimension reduction and label correlation and also points out the existing problems in the multi-label learning research. Finally, it summarizes several valuable research directions, which provides reference for the further research in this field. |
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| Keywords: | multi-label learning classification label correlation dimension reduction |
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