Dynamic adaptive ensemble case-based reasoning: application to stock market prediction

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dc.contributor.authorChun, Se-Hakko
dc.contributor.authorPark, Yoon-Jooko
dc.date.accessioned2013-03-06T19:27:58Z-
dc.date.available2013-03-06T19:27:58Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2005-04-
dc.identifier.citationEXPERT SYSTEMS WITH APPLICATIONS, v.28, pp.435 - 443-
dc.identifier.issn0957-4174-
dc.identifier.urihttp://hdl.handle.net/10203/88157-
dc.description.abstractThis paper proposes a new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectKNOWLEDGE DISCOVERY TECHNIQUES-
dc.subjectNEURAL-NETWORK ENSEMBLES-
dc.subjectCLASSIFICATION-
dc.titleDynamic adaptive ensemble case-based reasoning: application to stock market prediction-
dc.typeArticle-
dc.identifier.wosid000227546200004-
dc.identifier.scopusid2-s2.0-13644257906-
dc.type.rimsART-
dc.citation.volume28-
dc.citation.beginningpage435-
dc.citation.endingpage443-
dc.citation.publicationnameEXPERT SYSTEMS WITH APPLICATIONS-
dc.identifier.doi10.1016/j.eswa.2004.12-.004-
dc.contributor.nonIdAuthorChun, Se-Hak-
dc.type.journalArticleArticle-
dc.subject.keywordAuthordynamic ensemble case-based reasoning-
dc.subject.keywordAuthorartificial neural network-
dc.subject.keywordAuthorknowledge discovery-
dc.subject.keywordAuthordata mining-
dc.subject.keywordAuthorstock price prediction-
dc.subject.keywordAuthorlearning system-
dc.subject.keywordPlusKNOWLEDGE DISCOVERY TECHNIQUES-
dc.subject.keywordPlusNEURAL-NETWORK ENSEMBLES-
dc.subject.keywordPlusCLASSIFICATION-
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