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Article: Multi-label collective classification via Markov chain based learning method

TitleMulti-label collective classification via Markov chain based learning method
Authors
KeywordsMarkov chain with restart
Multi-label learning
Collective classification
Machine learning
Multi-label collective classification
Issue Date2014
Citation
Knowledge-Based Systems, 2014, v. 63, p. 1-14 How to Cite?
AbstractIn this paper, we study the problem of multi-label collective classification (MLCC) where instances are related and associated with multiple class labels. Such correlation of class labels among interrelated instances exists in a wide variety of data, e.g., a web page can belong to multiple categories since its semantics can be recognized in different ways, and the linked web pages are more likely to have the same classes than the unlinked pages. We propose an effective and novel Markov chain based learning method for MLCC problems. Our idea is to model the problem as a Markov chain with restart on transition probability graphs, and to propagate the ranking score of labeled instances to unlabeled instances based on the affinity among instances. The affinity among instances is set up by explicitly using the attribute features derived from the content of instances as well as the correlation features constructed from the links of instances. Intuitively, an instance which contains linked neighbors that are highly similar to the other instances with a high rank of a particular class label, has a high chance of this class label. Extensive experiments have been conducted on two DBLP datasets to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed algorithm is shown to be better than those of the binary relevance multi-label algorithm, collective classification algorithms (wvRN, ICA and Gibbs), and the ICML algorithm for the tested MLCC problems. © 2014 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/276989
ISSN
2017 Impact Factor: 4.396
2015 SCImago Journal Rankings: 2.140

 

DC FieldValueLanguage
dc.contributor.authorWu, Qingyao-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorYe, Yunming-
dc.contributor.authorLi, Xutao-
dc.contributor.authorShi, Ruichao-
dc.contributor.authorLi, Yan-
dc.date.accessioned2019-09-18T08:35:16Z-
dc.date.available2019-09-18T08:35:16Z-
dc.date.issued2014-
dc.identifier.citationKnowledge-Based Systems, 2014, v. 63, p. 1-14-
dc.identifier.issn0950-7051-
dc.identifier.urihttp://hdl.handle.net/10722/276989-
dc.description.abstractIn this paper, we study the problem of multi-label collective classification (MLCC) where instances are related and associated with multiple class labels. Such correlation of class labels among interrelated instances exists in a wide variety of data, e.g., a web page can belong to multiple categories since its semantics can be recognized in different ways, and the linked web pages are more likely to have the same classes than the unlinked pages. We propose an effective and novel Markov chain based learning method for MLCC problems. Our idea is to model the problem as a Markov chain with restart on transition probability graphs, and to propagate the ranking score of labeled instances to unlabeled instances based on the affinity among instances. The affinity among instances is set up by explicitly using the attribute features derived from the content of instances as well as the correlation features constructed from the links of instances. Intuitively, an instance which contains linked neighbors that are highly similar to the other instances with a high rank of a particular class label, has a high chance of this class label. Extensive experiments have been conducted on two DBLP datasets to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed algorithm is shown to be better than those of the binary relevance multi-label algorithm, collective classification algorithms (wvRN, ICA and Gibbs), and the ICML algorithm for the tested MLCC problems. © 2014 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofKnowledge-Based Systems-
dc.subjectMarkov chain with restart-
dc.subjectMulti-label learning-
dc.subjectCollective classification-
dc.subjectMachine learning-
dc.subjectMulti-label collective classification-
dc.titleMulti-label collective classification via Markov chain based learning method-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.knosys.2014.02.012-
dc.identifier.scopuseid_2-s2.0-84899955735-
dc.identifier.volume63-
dc.identifier.spage1-
dc.identifier.epage14-

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