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A Semisupervised Classification Approach for Multidomain Networks With Domain Selection
Chen, Chuan1,2; Xin, Jingxue3,4,5,6,7; Wang, Yong5,6,7; Chen, Luonan3,4; Ng, Michael K.2
2019
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume30Issue:1Pages:269-283
AbstractMultidomain 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.
KeywordDomain selection multidomain classification network integration semisupervised learning sparsity
DOI10.1109/TNNLS.2018.2837166
Language英语
Funding ProjectNational 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000454329300022
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32112
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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.
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