Title :
A more efficient classification scheme for ID3
Author :
Chai Rui-Min ; Wang Miao
Author_Institution :
Sch. of Electron. & Inf. Eng., Liaoning Tech. Univ., Huludao, China
Abstract :
Analyzed the principles and implementation steps of ID3 and the existing two improved ID3 algorithms proposed in [4] and [5]. ID3 has the shortcoming of inclining to choose attributes which have many values. Although the current two improved classification algorithms have solved the shortcoming of ID3, their classification time is not enough short, and their classification accuracy is not enough high. We proposed a new scheme. Our scheme solved the shortcoming of ID3 and improved the existing two algorithms effectively. Finally we use experiment to prove that the new scheme has shorter classification time and higher classification accuracy than ID3 and the existing two classification algorithms.
Keywords :
data mining; decision trees; pattern classification; ID3 algorithm; classification algorithm; data mining; decision tree generation algorithm; Algorithm design and analysis; Classification algorithms; Classification tree analysis; Data mining; Decision trees; Educational technology; Information entropy; Probability; ID3; attributes selection; data mining; decision tree;
Conference_Titel :
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6347-3
DOI :
10.1109/ICCET.2010.5486128