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Article: Exploring optimization of semantic relationship graph for multi-relational Bayesian classification

TitleExploring optimization of semantic relationship graph for multi-relational Bayesian classification
Authors
KeywordsWidth-first
Multi-relational classification
Naïve Bayesian classification
Semantic relationship graph
Depth-first
Feature selection
Issue Date2009
Citation
Decision Support Systems, 2009, v. 48, n. 1, p. 112-121 How to Cite?
AbstractIn recent years, there has been growing interest in multi-relational classification research and application, which addresses the difficulties in dealing with large relation search space, complex relationships between relations, and a daunting number of attributes involved. Bayesian Classifier is a simple but effective probabilistic classifier which has been shown to be able to achieve good results in most real world applications. Existing works for multi-relational Naïve Bayes classifier mainly focus on how to extend traditional flat Naïve Bayes classification method to multi-relational environment. In this paper, we look into issues concerned with how to increase the accuracy of multi-relational Bayesian classifier but still retain its efficiency. We develop a Semantic Relationship Graph (SRG) to describe the relationship between multiple tables and guide the search within relation space. Afterwards, we optimize the Semantic Relationship Graph by avoiding undesirable joins between relations and eliminating unnecessary attributes and relations. The experimental study on the real-world and synthetic databases shows that the proposed optimizing strategies make the multi-relational Naïve Bayesian classifier achieve improved accuracy by sacrificing a small amount of running time. © 2009 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/267577
ISSN
2019 Impact Factor: 4.721
2015 SCImago Journal Rankings: 2.262
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, H-
dc.contributor.authorLiu, H-
dc.contributor.authorHan, J-
dc.contributor.authorYin, X-
dc.contributor.authorHe, J-
dc.date.accessioned2019-02-22T04:08:25Z-
dc.date.available2019-02-22T04:08:25Z-
dc.date.issued2009-
dc.identifier.citationDecision Support Systems, 2009, v. 48, n. 1, p. 112-121-
dc.identifier.issn0167-9236-
dc.identifier.urihttp://hdl.handle.net/10722/267577-
dc.description.abstractIn recent years, there has been growing interest in multi-relational classification research and application, which addresses the difficulties in dealing with large relation search space, complex relationships between relations, and a daunting number of attributes involved. Bayesian Classifier is a simple but effective probabilistic classifier which has been shown to be able to achieve good results in most real world applications. Existing works for multi-relational Naïve Bayes classifier mainly focus on how to extend traditional flat Naïve Bayes classification method to multi-relational environment. In this paper, we look into issues concerned with how to increase the accuracy of multi-relational Bayesian classifier but still retain its efficiency. We develop a Semantic Relationship Graph (SRG) to describe the relationship between multiple tables and guide the search within relation space. Afterwards, we optimize the Semantic Relationship Graph by avoiding undesirable joins between relations and eliminating unnecessary attributes and relations. The experimental study on the real-world and synthetic databases shows that the proposed optimizing strategies make the multi-relational Naïve Bayesian classifier achieve improved accuracy by sacrificing a small amount of running time. © 2009 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofDecision Support Systems-
dc.subjectWidth-first-
dc.subjectMulti-relational classification-
dc.subjectNaïve Bayesian classification-
dc.subjectSemantic relationship graph-
dc.subjectDepth-first-
dc.subjectFeature selection-
dc.titleExploring optimization of semantic relationship graph for multi-relational Bayesian classification-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.dss.2009.07.004-
dc.identifier.scopuseid_2-s2.0-70350574561-
dc.identifier.volume48-
dc.identifier.issue1-
dc.identifier.spage112-
dc.identifier.epage121-
dc.identifier.isiWOS:000272366100012-

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