On Effective E-mail Classification via Neural Networks
On Effective E-mail Classification via Neural Networks
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DOI：
10.1007/11546924_9
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发表时间：
2005-08
期刊：
影响因子：
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通讯作者：
Bin Cui;Anirban Mondal;Jialie Shen;G. Cong;K. Tan
中科院分区：
文献类型：
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作者：
Bin Cui;Anirban Mondal;Jialie Shen;G. Cong;K. Tan
针对互联网上日益严重的垃圾邮件问题，提出了一种有效的电子邮件分类和净化方法。顺便说一下，电子邮件消息可以被建模为半结构化文档，由一组具有预定义语义的字段和许多可变长度的自由文本字段组成。我们提出的方法处理具有预定义的语义以及可变长度的自由文本字段，以获得更高的准确性。这项工作的主要贡献是双重的。首先，我们提出了一个新的模型，基于神经网络（NN）的个人电子邮件分类。特别是，我们将电子邮件文件视为一种特殊的纯文本文件，这意味着我们的功能集相对较大（因为不同的电子邮件文件中有数千个不同的术语）。其次，我们建议使用主成分分析（PCA）作为NN的预处理器，以减少数据的大小和维度，使输入数据变得更可分类，更快的NN模型中使用的训练过程的收敛。我们的性能评估的结果表明，该算法确实是有效的，在合理的精度进行过滤。
For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying and cleansing method in this paper. Incidentally, E-mail messages can be modelled as semi-structured documents consisting of a set of fields with pre-defined semantics and a number of variable length free-text fields. Our proposed method deals with both fields having pre-defined semantics as well as variable length free-text fields for obtaining higher accuracy. The main contributions of this work are two-fold. First, we present a new model based on the Neural Network (NN) for classifying personal E-mails. In particular, we treat E-mail files as a particular kind of plain text files, the implication being that our feature set is relatively large (since there are thousands of different terms in different E-mail files). Second, we propose the use of Principal Component Analysis (PCA) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifiable and faster for the convergence of the training process used in the NN model. The results of our performance evaluation demonstrate that the proposed algorithm is indeed effective in performing filtering with reasonable accuracy.