"In vivo" spam filtering

Author:

Fawcett Tom1

Affiliation:

1. Hewlett-Packard Laboratories, Palo Alto, CA

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Reference32 articles.

1. An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages

2. CAUBE. AU. Spam volume statistics. Web page: http://www.caube.org.au/spamstats.html 2002. CAUBE. AU. Spam volume statistics. Web page: http://www.caube.org.au/spamstats.html 2002.

Cited by 47 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Temporal Effects on Pre-trained Models for Language Processing Tasks;Transactions of the Association for Computational Linguistics;2022

2. Pairwise Learning for Imbalanced Data Classification;2021 International Conference on Computational Science and Computational Intelligence (CSCI);2021-12

3. Applications of Deep Learning in Intelligent Transportation Systems;Journal of Big Data Analytics in Transportation;2020-08

4. Spam filtering using a logistic regression model trained by an artificial bee colony algorithm;Applied Soft Computing;2020-06

5. Setting decision thresholds when operating conditions are uncertain;Data Mining and Knowledge Discovery;2019-02-16

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3