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Proceedings Paper

Learning the attribute selection measures for decision tree
Author(s): Xiaolin Chen; Jia Wu; Zhihua Cai
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Paper 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.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87842S (13 March 2013); doi: 10.1117/12.2021251
Show Author Affiliations
Xiaolin Chen, China Univ. of Geosciences (China)
Jia Wu, China Univ. of Geosciences (China)
Zhihua Cai, China Univ. of Geosciences (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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