Joint Feature Selection and Classification for Multilabel Learning

doi:10.1109/TCYB.2017.2663838

	Joint Feature Selection and Classification for Multilabel Learning
	Huang, Jun 1,2; Li, Guorong 1; Huang, Qingming 1,3; Wu, Xindong 1,4
	2018-03-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	48 期号:3 页码:876-889
摘要	Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.
关键词	Feature selection label correlation label-specific features multilabel classification shared features
DOI	10.1109/TCYB.2017.2663838
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61650202] ; National Basic Research Program of China (973 Program)[2015CB351800] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDJ-SSW-SYS013] ; Program for Changjiang Scholars and Innovative Research Team in University of the Ministry of Education, China[IRT13059] ; U.S. National Science Foundation[1652107]
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000424826800005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：128[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/2XEOYT63/5661
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Guorong; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101480, Peoples R China 2.Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan 243032, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Univ Louisiana Lafayette, Sch Comp & Informat, Lafayette, LA 70503 USA
推荐引用方式 GB/T 7714	Huang, Jun,Li, Guorong,Huang, Qingming,et al. Joint Feature Selection and Classification for Multilabel Learning[J]. IEEE TRANSACTIONS ON CYBERNETICS,2018,48(3):876-889.
APA	Huang, Jun,Li, Guorong,Huang, Qingming,&Wu, Xindong.(2018).Joint Feature Selection and Classification for Multilabel Learning.IEEE TRANSACTIONS ON CYBERNETICS,48(3),876-889.
MLA	Huang, Jun,et al."Joint Feature Selection and Classification for Multilabel Learning".IEEE TRANSACTIONS ON CYBERNETICS 48.3(2018):876-889.