"In vivo" spam filtering: a challenge problem for KDD
"In vivo" spam filtering: a challenge problem for KDD
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“体内”垃圾邮件过滤：KDD 面临的挑战
DOI：
10.1145/980972.980990
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Tom Fawcett
中科院分区：
文献类型：
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作者：
Tom Fawcett
垃圾邮件，也被称为未经请求的商业电子邮件(UCE)，是电子邮件通信的祸害。许多数据挖掘研究人员已经解决了垃圾邮件的检测问题，通常是将其视为静态文本分类问题。诚然，体内垃圾邮件过滤的特点使其成为一个丰富且具有挑战性的数据挖掘领域。事实上，具有这些特征的真实世界数据集通常很难获得和共享。本文展示了其中的一些特征，并认为研究人员应该将体内垃圾邮件过滤作为研究它们的可访问领域。
Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True in vivo spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics are typically difficult to acquire and to share. This paper demonstrates some of these characteristics and argues that researchers should pursue in vivo spam filtering as an accessible domain for investigating them.