DocumentCode
1947768
Title
Data-driven decision tree learning algorithm based on rough set theory
Author
Yin, Desheng ; Wang, Guoyin ; Wu, Yu
Author_Institution
Inst. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun., China
fYear
2005
fDate
19-21 May 2005
Firstpage
579
Lastpage
584
Abstract
Decision tree pre-pruning is an effective method to solve the over-fitting problem in decision tree learning process. However, it is difficult to estimate the exact time to stop the growing process of a decision tree, which limits the developments and applications of this method. In this paper, the growing of a decision tree is controlled by the uncertainty of a decision table, and a data-driven learning algorithm for decision tree pre-pruning is developed.
Keywords
data mining; decision tables; decision trees; learning (artificial intelligence); rough set theory; data-driven decision tree learning algorithm; decision table uncertainty; decision tree prepruning; rough set theory; Automatic control; Computer science; Decision trees; Knowledge acquisition; Machine learning algorithms; Measurement uncertainty; Process control; Set theory; Testing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Active Media Technology, 2005. (AMT 2005). Proceedings of the 2005 International Conference on
Print_ISBN
0-7803-9035-0
Type
conf
DOI
10.1109/AMT.2005.1505426
Filename
1505426
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