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dc.contributor.authorBadulescu, Laviniu Aurelian
dc.contributor.editorNicolae, Ileana Dianaen_US
dc.contributor.editorDoicaru, Elenaen_US
dc.date.accessioned2008-07-07T00:00:01Z
dc.date.available2010-06-18T23:28:16Z
dc.date.issued2007en_US
dc.date.submitted2008-07-07en_US
dc.identifier.citationATTRIBUTE SELECTION MEASURE IN DECISION TREE GROWING 2007, 2(1):1-6en_US
dc.identifier.urihttp://hdl.handle.net/10150/105610
dc.description.abstractOne of the major tasks in Data Mining is classification. The growing of Decision Tree from data is a very efficient technique for learning classifiers. The selection of an attribute used to split the data set at each Decision Tree node is fundamental to properly classify objects; a good selection will improve the accuracy of the classification. In this paper, we study the behavior of the Decision Trees induced with 14 attribute selection measures over three data sets taken from UCI Machine Learning Repository.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherUniversitaria Publishing Houseen_US
dc.subjectData Miningen_US
dc.subjectClassificationen_US
dc.subjectComputer Scienceen_US
dc.subject.otherdecision treesen_US
dc.subject.otherclassificationen_US
dc.subject.othererror ratesen_US
dc.titleATTRIBUTE SELECTION MEASURE IN DECISION TREE GROWINGen_US
dc.typeConference Paperen_US
refterms.dateFOA2018-08-21T13:27:22Z
html.description.abstractOne of the major tasks in Data Mining is classification. The growing of Decision Tree from data is a very efficient technique for learning classifiers. The selection of an attribute used to split the data set at each Decision Tree node is fundamental to properly classify objects; a good selection will improve the accuracy of the classification. In this paper, we study the behavior of the Decision Trees induced with 14 attribute selection measures over three data sets taken from UCI Machine Learning Repository.


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