Title :
A decision tree generation algorithm based on maximum similarity
Author :
Xinmeng Zhang ; Shengyi Jiang
Author_Institution :
Cisco Sch. of Inf., Guangdong Univ. of Foreign Studies, Guangzhou, China
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.
Keywords :
data mining; decision trees; pattern classification; decision tree feature selection strategy; decision tree generation algorithm; fsms; maximum similarity; node splitting; splitting attribute; Accuracy; Classification algorithms; Decision trees; Educational institutions; Mathematical model; Rain; Training; Classification; data mining; decision tree; similarity;
Conference_Titel :
Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
Conference_Location :
Jilin
Print_ISBN :
978-1-61284-719-1
DOI :
10.1109/MEC.2011.6025641