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

doi:10.1109/TKDE.2016.2608339

	Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification
	Huang, Jun 1,2; Li, Guorong 1,2; Huang, Qingming 1,2,3; Wu, Xindong 4,5
	2016-12-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	28 期号:12 页码:3309-3323
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.
关键词	Multi-label classification label correlation feature selection
DOI	10.1109/TKDE.2016.2608339
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000388214700013
出版者	IEEE COMPUTER SOC
引用统计	被引频次：162[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7939
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.