使用机器学习方法识别和过滤Web垃圾邮件

Dawei Zhang, Yanyu Liu
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摘要

为了增强对互联网垃圾邮件的过滤,提高互联网用户的体验,本文提出利用向量空间模型将电子邮件文本转化为向量特征,构造二维矩阵,并利用卷积神经网络(CNN)对互联网垃圾邮件进行识别。在仿真实验中,将CNN与支持向量机(SVM)和后向传播神经网络(BPNN)两种分类器进行比较。最终结果表明,以CNN为分类器的垃圾邮件识别算法比以SVM和BPNN为分类器的算法具有更好的识别性能,并且在识别成本和时间上更有优势;此外,当提取的特征个数为15时,CNN的识别性能最好。
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Identification and Filtering of Web Spams Using a Machine Learning Method
In order to enhance the filtering of spam on the Internet and improve the experience of Internet users, this paper proposed to convert the email text into vector features using the vector space model, constructed a two-dimensional matrix, and used a convolutional neural network (CNN) to identify spam on the Internet. The CNN was compared with other two classifiers, support vector machine (SVM), and backward-propagation neural network (BPNN), in simulation experiments. The final results showed that the spam recognition algorithm with CNN as the classifier had better recognition performance than the algorithms with SVM and BPNN classifiers and was also more advantageous in terms of recognition cost and time for spam; in addition, the CNN had the best recognition performance when the number of extracted features was 15.
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