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Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification

文献类型：期刊论文

作者Huang, Jun1,2; Li, Guorong1,2; Huang, Qingming1,2,3; Wu, Xindong4,5
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2016-12-01
卷号28期号:12页码:3309-3323
关键词Multi-label classification label correlation feature selection
ISSN号1041-4347
DOI10.1109/TKDE.2016.2608339
英文摘要Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework, and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent Labels in a sparse stacking way, denoted as LLSF-DL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods.
资助项目National Basic Research Program of China (973 Program)[2012CB316400] ; National Basic Research Program of China (973 Program)[2015CB351802] ; Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China[IRT13059] ; National Natural Science Foundation of China[61303153] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61229301] ; National Natural Science Foundation of China[61620106009] ; Bureau of Frontier Science and Education of Chinese Academy of Sciences[QYZDJ-SSW-SYS013]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000388214700013
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/7938]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Huang, Jun
作者单位1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101480, Peoples R China
2.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
4.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China
5.Univ Vermont, Dept Comp Sci, 33 Colchester Ave, Burlington, VT 05405 USA
推荐引用方式
GB/T 7714		 Huang, Jun,Li, Guorong,Huang, Qingming,et al. Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2016,28(12):3309-3323.
APA Huang, Jun,Li, Guorong,Huang, Qingming,&Wu, Xindong.(2016).Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,28(12),3309-3323.
MLA Huang, Jun,et al."Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28.12(2016):3309-3323.

入库方式： OAI收割

来源：计算技术研究所

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