摘要
电子邮件分类有利于垃圾邮件的过滤,节省网络资源。为了提高邮件分类的精度,提出了改进支持向量机的电子邮件分类器模型。首先提取电子邮件的原始特征,并采用主成分分析法对特征进行选择,减少特征数量,提高邮件分类效率;然后采用支持向量机建立电子邮件分类器,并对传统支持向量机参数选择方法进行改进,改善邮件分类效果,最后采用邮件分类的标准数据库——UCI进行性能分析。结果表明,改进支持向量机解决了当前电子邮件分类模型的不足,获得了理想的电子邮件分类效果,分类结果可以帮助管理人员拦截垃圾邮件。
The E_mail classification is conducive to filter out the spam mail and save the network resources. In order to im-prove the accuracy of the E_mail classification, an E_mail classifier model using improved support vector machine is proposed.The original features of E_mail are extracted, and selected with the principal component analysis to reduce the feature quantityand improve the E_mail classification efficiency. The support vector machine is used to establish the E_mail classifier. The pa-rameter selection method of the traditional support vector machine was improved to perfect the E_mail classification effect. Thestandard database UCI of the E_mail classification is used to analyze the classification performance. The results show that the im-proved support vector machine has solved the insufficient of the current Email classification model, and obtained the satisfiedE_mail classification effect, which can help managers to block the spam mail.
作者
张洁
ZHANG Jie(Department of Information Technology, Qiongtai Normal College, Haikou 571127, China)
出处
《现代电子技术》
北大核心
2017年第1期77-79,82,共4页
Modern Electronics Technique
关键词
电子邮件
分类模型
特征提取
垃圾邮件
主成分分析
E_mail
classification model
feature extraction
spam mail
principal component analysis
