Learning the attribute selection measures for decision tree

Chen, X; Wu, J; Cai, Z

Learning the attribute selection measures for decision tree

Chen, X Wu, J Cai, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings of SPIE - The International Society for Optical Engineering, 2013, 8784
Issue Date:: 2013-07-10

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Field	Value	Language
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Cai, Z	en_US
dc.date.issued	2013-07-10	en_US
dc.identifier.citation	Proceedings of SPIE - The International Society for Optical Engineering, 2013, 8784	en_US
dc.identifier.isbn	9780819495884	en_US
dc.identifier.issn	0277-786X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120791
dc.description.abstract	Decision tree has most widely used for classification. However the main influence of decision tree classification performance is attribute selection problem. The paper considers a number of different attribute selection measures and experimentally examines their behavior in classification. The results show that the choice of measure doesn't affect the classification accuracy, but the size of the tree is influenced significantly. The main effect of the new attribute selection measures which base on normal gain and distance is that they generate smaller trees than traditional attribute selection measures. © 2013 SPIE.	en_US
dc.relation.ispartof	Proceedings of SPIE - The International Society for Optical Engineering	en_US
dc.relation.isbasedon	10.1117/12.2021251	en_US
dc.title	Learning the attribute selection measures for decision tree	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8784	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	8784	en_US

Abstract:

Decision tree has most widely used for classification. However the main influence of decision tree classification performance is attribute selection problem. The paper considers a number of different attribute selection measures and experimentally examines their behavior in classification. The results show that the choice of measure doesn't affect the classification accuracy, but the size of the tree is influenced significantly. The main effect of the new attribute selection measures which base on normal gain and distance is that they generate smaller trees than traditional attribute selection measures. © 2013 SPIE.

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http://hdl.handle.net/10453/120791