NDLI: A decision tree generation algorithm based on maximum similarity

Content Provider	IEEE Xplore Digital Library
Author	Xinmeng Zhang Shengyi Jiang
Copyright Year	2011
Description	Author affiliation: Cisco School of Informatics, Guangdong University of Foreign Studies, Guang Zhou, China (Xinmeng Zhang; Shengyi Jiang)
Abstract	Node splitting is good or bad depends on the measure method of the impurity. We propose a new decision tree feature selection strategy based on maximum similarity, called fsms. First, splitting the dataset into subset according to each attribute value, calculating the sum of average similarity of each subset, then selecting the attribute with the maximum similarity as the best splitting attribute. Experimental results show, After tested in multiple test dataset, The decision tree constructed by the algorithm is better than Some classic algorithms such as id3, c4.5 in The classification precision, and less affected by the size of dataset.
Starting Page	1032
Ending Page	1035
File Size	241709
Page Count	4
File Format	PDF
ISBN	9781612847191
e-ISBN	9781612847221
DOI	10.1109/MEC.2011.6025641
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Rain Similarity Classification Educational institutions Classification algorithms Decision trees Mathematical model Data mining Decision tree
Content Type	Text
Resource Type	Article

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A decision tree generation algorithm based on maximum similarity