[1]赵卫东,盛昭瀚,何建敏.粗糙集在决策树生成中的应用[J].东南大学学报(自然科学版),2000,30(4):132-137.[doi:10.3969/j.issn.1001-0505.2000.04.027]
 Zhao Weidong,Sheng Zhaohan,He Jianmin.Application of Rough Sets to the Designing of Decision Trees[J].Journal of Southeast University (Natural Science Edition),2000,30(4):132-137.[doi:10.3969/j.issn.1001-0505.2000.04.027]
点击复制

粗糙集在决策树生成中的应用()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
30
期数:
2000年第4期
页码:
132-137
栏目:
自动化
出版日期:
2000-07-20

文章信息/Info

Title:
Application of Rough Sets to the Designing of Decision Trees
作者:
赵卫东 盛昭瀚 何建敏
东南大学经济管理学院,南京 210096
Author(s):
Zhao Weidong Sheng Zhaohan He Jianmin
College of Economics and Management, Southeast University, Nanjing 210096
关键词:
粗糙集 决策树 优化
Keywords:
rough set decision tree optimization
分类号:
TP18
DOI:
10.3969/j.issn.1001-0505.2000.04.027
摘要:
决策树是归纳学习的重要形式,建造高质量的决策树的关键是选择合适的属性.本文针对ID3算法对属性间的相依性强调不够等问题,利用粗糙集理论,提出一种新的启发式函数——分辨率构造决策树.分辨率本质上是相关属性的组合,但不是简单的属性合取.它不仅考虑了属性之间的依赖性,还兼顾了分类的种数.大量实例表明,本文的方法明显优于ID3算法.
Abstract:
Decision trees are important forms of inductive learning. The key to building a good decision tree lies in the reasonable choice of attributes. In relation to problems existing in ID3 algorithm, such as overlooking the interconnection between attributes, the paper proposes a new super-attribute, resolution, for the optimization of decision trees based on rough sets. The super-attribute, in essence, is the integration of relational attributes, but not simple integration. It stresses both the dependency of attributes and the number of classification. Some examples are given to show that the method herein is obviously better than ID3 algorithm.

参考文献/References:

[1] Quinlan J R.Induction of decision trees.Machine Learning,1986,1(1):81~106
[2] Hong J R.AE1:an extension approximate method for general covering problem.International Journal of Computer and Information Science,1985,14(6):421~437
[3] 刘小虎,李生.决策树的优化算法.软件学报,1998,9(10):797~800
[4] Pagallo G,Haussler D.Boolean feature discovery in empirical learning.Machine Learning,1990,5:71~99
[5] Brodley C E,Utgoff P E.Multivariate decision trees.Machine Learning,1995,19:45~77
[6] 苗夺谦,王珏.基于粗糙集的多变量决策树构造方法.软件学报,1997,8(6); 425~431
[7] Pawlak Z.Rough sets:theoretical aspects of reasoning about data.Netherlands:Kluwer Academic Publishers,1991

相似文献/References:

[1]窦东阳,杨建国,李丽娟,等.基于规则的神经网络在模式分类中的应用[J].东南大学学报(自然科学版),2011,41(3):482.[doi:10.3969/j.issn.1001-0505.2011.03.010]
 Dou Dongyang,Yang Jianguo,Li Lijuan,et al.Application of rule-based neural network in pattern classification[J].Journal of Southeast University (Natural Science Edition),2011,41(4):482.[doi:10.3969/j.issn.1001-0505.2011.03.010]

备注/Memo

备注/Memo:
基金项目: 江苏省自然科学基金资助项目(7760573002).
第一作者:男, 1971年生, 博士研究生.
更新日期/Last Update: 2000-07-20