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竞争选择分裂属性的决策树分类模型

A Decision-Tree Classifier Model of Competition in Choosing Split Attribute
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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。 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.
作者 房立 黄泽宇
机构地区 北京交通大学计算机与信息技术学院
出处 《计算机技术与发展》 2006年第8期106-109,共4页 Computer Technology and Development
关键词 决策树 信息增益 增益比率 Gini索引 Goodman-Kruskal关联索引 decision-tree entropy gain gain ratio gini index Goodman-Kruskal association index
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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计算机技术与发展
2006年 第8期

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