13 March 2013 Learning the attribute selection measures for decision tree
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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.
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Xiaolin Chen, Xiaolin Chen, Jia Wu, Jia Wu, Zhihua Cai, Zhihua Cai, } "Learning the attribute selection measures for decision tree", 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; https://doi.org/10.1117/12.2021251
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