Persistent Link:
http://hdl.handle.net/10150/105610
Title:
ATTRIBUTE SELECTION MEASURE IN DECISION TREE GROWING
Author:
Badulescu, Laviniu Aurelian
Editors:
Nicolae, Ileana Diana; Doicaru, Elena
Citation:
ATTRIBUTE SELECTION MEASURE IN DECISION TREE GROWING 2007, 2(1):1-6
Publisher:
Universitaria Publishing House
Issue Date:
2007
URI:
http://hdl.handle.net/10150/105610
Submitted date:
2008-07-07
Abstract:
One 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.
Type:
Conference Paper
Language:
en
Keywords:
Data Mining; Classification; Computer Science
Local subject classification:
decision trees; classification; error rates

Full metadata record

DC FieldValue Language
dc.contributor.authorBadulescu, Laviniu Aurelianen_US
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.en_US
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
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.