[1]房立 黄泽宇.竞争选择分裂属性的决策树分类模型[J].计算机技术与发展,2006,(08):106-109.
　FANG Li,HUANG Ze-yu.A Decision - Tree Classifier Model of Competition in Choosing Split Attribute[J].,2006,(08):106-109.
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竞争选择分裂属性的决策树分类模型()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2006年08期
页码:
106-109
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
A Decision - Tree Classifier Model of Competition in Choosing Split Attribute
文章编号:
1673-629X（2006）08-0106-04
作者:
房立 黄泽宇
北京交通大学计算机与信息技术学院
Author(s):
FANG Li; HUANG Ze-yu
Department of Computer Science and Information Technology, Jiaotong University
关键词:
决策树; 信息增益; 增益比率; Gini索引; Goodman-Kruskal关联索引
Keywords:
decision-tree; entropy gain; gain ratio; gini index; Goodman-Kruskal association index
分类号:
TP311.13
文献标志码:
A
摘要:
构建决策树分类器关键是选择分裂属性。通过分析信息增益和增益比率、Gini索引、基于Goodman-Kruskal关联索引这三种选择分裂属性的标准，提出了一种改进经典决策树分类器C4＋5算法的方法（竞争选择分裂属性的决策树分类模型），它综合三种选择分裂属性的标准，通过竞争机制选择最佳分裂属性。实验结果表明它在大多数情况下，使得不牺牲分类精确度而获得更小的决策树成为了可能
Abstract:
The construction of decision- tree is centered on the selection algorithm of an attribute that generates a partition of the subsets of the training database that is located in the node about to be split. On the basis of analyzing three techniques for choosing the splitting attributes including the entropy gain and the gain ratio, the gini index and Goodman - Knaskal association index, propose a strategy to improve on classical decision - tree classifier C4.5 arithmetic（a decision-tree classifier model of competition in choosing split attribute）. Experimental results show it is possible, in most cases, to obtain smaller decision trees without sacrificing accuracy

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备注/Memo

备注/Memo:
房立（1980-），女，天津人，硕士研究生，主要研究领域为机器学习、数据挖掘
更新日期/Last Update: 1900-01-01