粗糙集与决策树在电子邮件分类与过滤中的应用  

Application of rough set and decision tree in e-mail classification and filtering

作　　者：邓春燕[1,2] 陶多秀[3] 吕跃进[2] 

机构地区：[1]广西河池学院计算机与信息科学系,广西宜州546300 [2]广西大学数学与信息科学学院,南宁530004 [3]广西大学电气工程学院,南宁530004

出　　处：《计算机工程与应用》2009年第16期138-140,184,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.70861001);广西研究生科研创新项目(No.2008105930701)~~

摘　　要：垃圾邮件的识别与过滤是目前研究的热点问题之一。而粗糙集是一种新的处理模糊和不确定性知识的数据分析工具,已被成功地应用到许多有关分类的领域。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。通过与朴素贝叶斯模型及SVM的比较,表明提出的基于RS-DT的模型可以降低把正常邮件错分为垃圾邮件的比率,提高过滤系统的自学习能力。Spam identification and filtering is one of the hot issues.And the rough set is a new data analysis tool to deal with ambiguity and uncertainty of knowledge;it has been successfully applied to many areas of classification.Combining rough sets with decision tree,a spare filtering solution based on rough sets and decision tree（RS-DT） was proposed.The feasibility of the solution was indicated by the experiments on the public email corpus.Comparison experiments were also made between SVM classifier, Bayes classifier and RS-DT model.The results show that the RS-DT model can not only reduce the error rate of judging the normal email as spare,but also improve adaptive learning of the filtration system.

关 键 词：垃圾邮件 粗糙集 数据挖掘 决策树 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]