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Enterprise Email Classification Based on Social Network Features

机译:基于社交网络特征的企业电子邮件分类

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With the popularity of multimedia and network technologies, it is now often to attach large size of multimedia dataset to emails. However, delivering large volume of multimedia data over an enterprise email system can easily bring down the quality of overall network service. Moreover, without some sort of restrictions, many enterprises found that the network resource was occupied for personal interests. The business communication over emails thus suffers undesirable delays and cause damages to businesses. The competition to use email service therefore become an issue that many enterprises have to deal with. Obviously, enterprises should manage the email service so that business emails have the priority over personal usages. This management requires an effective methodology to classify enterprise emails into official and private emails, and the development of the method is the goal of this work. To achieve the accuracy of a desired classification methodology, we normally anticipated the developed method to survey as much information as possible. On the other hand, monitoring details of the email contents not only can decrease the performance of the method, but it also may violate the privacy rights that many legal regulation systems now protect. The balance of pursuing accurate classification and protecting privacy rights becomes a challenge for this problem. With the discussed challenges in mind, we develop an email classification method based on social features, rather than surveying the email contents. To the best of our knowledge, this paper is the first study to address the aforementioned problems. We obtain social features from emails to represent the input vector of support vector machine (SVM) classifier. Preliminary results show that our methodology can classify emails with a high accuracy. Compared with the other content-based feature of email, our work shows that exploring social features is a promising direction to solve similar email classification probl--ems.
机译:随着多媒体和网络技术的普及,现在经常需要将大量的多媒体数据集附加到电子邮件中。但是,通过企业电子邮件系统传递大量多媒体数据会很容易降低整个网络服务的质量。而且,在没有某种限制的情况下,许多企业发现网络资源是出于个人利益而占用的。因此,通过电子邮件进行的业务通信会遭受不希望的延迟,并给业务造成损害。因此,使用电子邮件服务的竞争成为许多企业必须处理的问题。显然,企业应该管理电子邮件服务,以便企业电子邮件优先于个人使用。这种管理需要一种有效的方法来将企业电子邮件分类为官方电子邮件和私人电子邮件,该方法的开发是这项工作的目标。为了达到所需分类方法的准确性,我们通常期望开发出的方法能够调查尽可能多的信息。另一方面,监视电子邮件内容的详细信息不仅会降低方法的性能,而且还可能侵犯许多法律法规系统现在保护的隐私权。追求准确分类和保护隐私权之间的平衡成为此问题的挑战。考虑到所讨论的挑战,我们开发了一种基于社交功能的电子邮件分类方法,而不是调查电子邮件内容。据我们所知,本文是针对上述问题的第一个研究。我们从电子邮件中获取社交功能,以表示支持向量机(SVM)分类器的输入向量。初步结果表明,我们的方法可以对电子邮件进行高精度分类。与其他基于内容的电子邮件功能相比,我们的工作表明,探索社交功能是解决类似电子邮件分类问题的有前途的方向- -- ems。

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