Learning the attribute selection measures for decision tree

Xiaolin Chen, Zhihua Cai, Jia Wu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationFifth International Conference on Machine Vision (ICMV 2012)
Subtitle of host publicationalgorithms, pattern recognition, and basic technologies
EditorsYulin Wang, Liansheng Tan, Jianhong Zhou
PublisherSPIE
Pages87842S
Number of pages8
Volume8784
ISBN (Electronic)9780819495884
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event5th International Conference on Machine Vision (ICMV 2012) - Wuhan, China
Duration: 20 Oct 201221 Oct 2012

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume8784
ISSN (Electronic)0277-786X

Conference

Conference5th International Conference on Machine Vision (ICMV 2012)
CountryChina
CityWuhan
Period20/10/1221/10/12

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