Data-driven decision tree learning algorithm based on rough set theory

被引：4

作者：

Yin, DS ^{[1
]}

Wang, GY ^{[1
]}

Wu, Y ^{[1
]}

机构：

[1] Chongqing Univ Posts & Telecommun, Inst Comp Sci & Technol, Chongqing 400065, Peoples R China

来源：

PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ACTIVE MEDIA TECHNOLOGY (AMT 2005) | 2005年

关键词：

rough set; data-driven; decision tree; pre-pruning;

D O I：

10.1109/AMT.2005.1505426

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 prepruning is developed.

引用

页码：579 / 584

页数：6

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