Decision trees for multiple abstraction levels of data

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dc.contributor.authorLee, Doheonko
dc.contributor.authorJeong, Mko
dc.contributor.authorWon, YKko
dc.date.accessioned2013-03-04T03:46:01Z-
dc.date.available2013-03-04T03:46:01Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2001-
dc.identifier.citationCOOPERATIVE INFORMATION AGENTS V, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, v.2182, pp.76 - 87-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10203/81790-
dc.description.abstractSince the data is collected from disparate sources in many actual data mining environments, it is common to have data values in different abstraction levels. This paper shows that such multiple abstraction levels of data can cause undesirable effects in decision tree classification. After explaining that equalizing abstraction levels by force cannot provide satisfactory solutions of this problem, it presents a method to utilize the data as it is. The proposed method accommodates the generalization/specialization relationship between data values in both of the construction and the class assignment phases of decision tree classification. The experimental results show that the proposed method reduces classification error rates significantly when multiple abstraction levels of data are involved.-
dc.languageEnglish-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.subjectFRAMEWORK-
dc.titleDecision trees for multiple abstraction levels of data-
dc.typeArticle-
dc.identifier.wosid000180978200009-
dc.type.rimsART-
dc.citation.volume2182-
dc.citation.beginningpage76-
dc.citation.endingpage87-
dc.citation.publicationnameCOOPERATIVE INFORMATION AGENTS V, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE-
dc.contributor.localauthorLee, Doheon-
dc.contributor.nonIdAuthorJeong, M-
dc.contributor.nonIdAuthorWon, YK-
dc.type.journalArticleArticle; Proceedings Paper-
dc.subject.keywordPlusFRAMEWORK-
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