Rough set-based decision tree using a core attribute

被引：6
|
作者：
Han, Sang-Wook [1 ]
Kim, Jae-Yearn [1 ]
机构：
[1] Hanyang Univ, Dept Ind Engn, Seoul 133791, South Korea
来源：
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING | 2008年 / 7卷 / 02期
关键词：
core; reduct; decision tree; discernibility matrix; rough set;
D O I：
10.1142/S0219622008002946
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Decision trees are widely used in machine learning and artificial intelligence. In this paper, we extend previous research and present a new decision tree classification algorithm that uses a rough set theory to produce classification rules. Our algorithm is based on core attributes and on comparing the values of attributes between objects. Our experiments compared the performance of the Iterative Dichotomiser 3 (ID3) algorithm, C4.5, and the proposed decision tree algorithm to demonstrate its accuracy and ability to simplify rules.
引用
收藏
页码：275 / 290
页数：16
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