This paper presents a new method of WEB text categorization rule extraction based on the CHI value theory, rough set theory and decision tree.
本文根据CHI值原理、粗集理论和决策树原理，提出了一种抽取Web文本分类规则的新方法。
We propose a new approach to multivariate decision tree construction based on knowledge roughness in rough set instead of information entropy as usual.
提出了一种基于粗糙集中知识粗糙度的构建多变量决策树的算法。
Value reduction in rough set theory and decision tree in data mining are effectively used in the classification, but each of them has shortcomings.
粗糙集理论中的值约简和数据挖掘领域中的决策树都是有效的分类方法，但二者都有其局限性。
Combining rough sets with decision tree, a spam filtering solution based on rough sets and decision tree (RS-DT) was proposed.
将粗糙集与决策树结合，提出一个基于RS - DT的邮件分类方案与模型，并进行了实验及结果分析。
The multivariate decision tree technique based on rough set is proved helpful in aided decision-making for electric marketing.
可见本文的方法对电力市场营销分析具有较强的辅助决策意义。
In the process of constructing a decision tree, weighted mean roughness, a new concept based on rough set theory which is regarded as the criteria for choosing attributes is applied.
介绍了决策树算法的含义和构筑方法，对基于加权平均粗糙度构造决策树算法进行改进，通过实例说明了改进算法的优势。
Rough Set and Decision Tree, usually used to analyze the data and the formation of predictive models, are important methods of knowledge discovery and learning.
粗糙集和决策树是知识挖掘和学习的重要方法，通常用来分析数据和形成预测模型。
Rough Set and Decision Tree, usually used to analyze the data and the formation of predictive models, are important methods of knowledge discovery and learning.
粗糙集和决策树是知识挖掘和学习的重要方法，通常用来分析数据和形成预测模型。
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