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ifile: An Application of Machine Learning to E-Mail Filtering (2000)
| Venue: | Proc. KDD Workshop on Text Mining |
| Citations: | 51 - 0 self |
BibTeX
@INPROCEEDINGS{Rennie00ifile:an,
author = {Jason D. M. Rennie},
title = {ifile: An Application of Machine Learning to E-Mail Filtering},
booktitle = {Proc. KDD Workshop on Text Mining},
year = {2000}
}
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Abstract
The rise of the World Wide Web and the ever-increasing amounts of machine-readable text has caused text classification to become a important aspect of machine learning. One specific application that has the potential to affect almost every user of the Internet is e-mail filtering. The WorldTalk Corporation estimates that over 60 million business people use e-mail [6]. Many more use e-mail purely on a personal basis and the pool of e-mail users is growing daily. And yet, automated techniques for learning to filter e-mail have yet to significantly affect the e-mail market. Here, I attack problems that plague practical e-mail ltering and suggest solutions that will bring us closer to the acceptance of using automated classification techniques to filter personal e-mail. I also present a filtering system, ifile, that is both effective and efficient, and which has been adapted to a popular e-mail client. Results are presented from a number of experiments and show that a system such as ifile could become a...
Keyphrases
e-mail filtering machine learning business people world wide web text classification machine-readable text e-mail market specific application worldtalk corporation ever-increasing amount personal basis practical e-mail ltering filtering system automated classification technique important aspect personal e-mail popular e-mail client e-mail user
