A Semisupervised Classification Approach for Multidomain Networks With Domain Selection

doi:10.1109/TNNLS.2018.2837166

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	A Semisupervised Classification Approach for Multidomain Networks With Domain Selection
	Chen, Chuan 1,2; Xin, Jingxue 3,4,5,6,7; Wang, Yong5,6,7 ; Chen, Luonan 3,4; Ng, Michael K.2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2019
卷号	30 期号:1 页码:269-283
关键词	Domain selection multidomain classification network integration semisupervised learning sparsity
ISSN号	2162-237X
DOI	10.1109/TNNLS.2018.2837166
英文摘要	Multidomain network classification has attracted significant attention in data integration and machine learning, which can enhance network classification or prediction performance by integrating information from different sources. Despite the previous success, existing multidomain network learning methods usually assume that different views are available for the same set of instances, and thus, they seek a consistent classification result for all domains. However, in many real-world problems, each domain has its specific instance set, and one instance in one domain may correspond to multiple instances in another domain. Moreover, due to the rapid growth of data sources, different domains may not be relevant to each other, which asks for selecting domains relevant to the target/focused domain. A key challenge under this setting is how to achieve accurate prediction by integrating different data representations without losing data information. In this paper, we propose a semisupervised classification approach for a multidomain network based on label propagation, i.e., multidomain classification with domain selection (MCS), which can deal with the cross-domain information and different instance sets in domains. In particular, with sparse weight properties, the proposed MCS can automatically identify those domains relevant to our target domain by assigning them higher weights than the other irrelevant domains. This not only significantly improves a classification accuracy but also helps to obtain optimal network partition for the target domain. From the theoretical viewpoint, we equivalently decompose MCS into two simpler subproblems with analytical solutions, which can be efficiently solved by their computational procedures. Extensive experimental results on both synthetic and real-world data sets empirically demonstrate the advantages of the proposed approach in terms of both prediction performance and domain selection ability.
资助项目	National Key Research and Development Program of China[2017YFA0505500] ; National Key Research and Development Program of China[2016YFB1000101] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB13040700] ; National Natural Science Foundation of China[91529303] ; National Natural Science Foundation of China[31771476] ; National Natural Science Foundation of China[81471047] ; National Natural Science Foundation of China[91730301] ; National Natural Science Foundation of China[61671444] ; National Natural Science Foundation of China[61621003]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000454329300022
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/32111]
专题	应用数学研究所
通讯作者	Ng, Michael K.
作者单位	1.Sun Yat Sen Univ, Natl Engn Res Ctr Digital Life, Sch Data & Comp Sci, Guangzhou 510006, Guangdong, Peoples R China 2.Hong Kong Baptist Univ, Dept Math, Hong Kong, Peoples R China 3.Chinese Acad Sci, Key Lab Syst Biol, CAS Ctr Excellence Mol Cell Sci, Inst Biochem & Cell Biol,Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China 4.Chinese Acad Sci, CAS Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China 5.Chinese Acad Sci, Key Lab Management Decis & Informat Syst, Natl Ctr Math & Interdisciplinary Sci, Ctr Excellence Math Sci,Acad Math & Syst Sci, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 7.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Chen, Chuan,Xin, Jingxue,Wang, Yong,et al. A Semisupervised Classification Approach for Multidomain Networks With Domain Selection[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(1):269-283.
APA	Chen, Chuan,Xin, Jingxue,Wang, Yong,Chen, Luonan,&Ng, Michael K..(2019).A Semisupervised Classification Approach for Multidomain Networks With Domain Selection.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(1),269-283.
MLA	Chen, Chuan,et al."A Semisupervised Classification Approach for Multidomain Networks With Domain Selection".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.1(2019):269-283.