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Joint Feature Selection and Classification for Multilabel Learning

文献类型：期刊论文

作者Huang, Jun2,3; Li, Guorong3; Huang, Qingming1,3; Wu, Xindong3,4
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2018-03-01
卷号48期号:3页码:876-889
关键词Feature selection label correlation label-specific features multilabel classification shared features
ISSN号2168-2267
DOI10.1109/TCYB.2017.2663838
英文摘要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.
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
语种英语
WOS记录号WOS:000424826800005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/5660]  
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan 243032, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101480, 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.

入库方式： OAI收割

来源：计算技术研究所

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