An empirical study on email classification using supervised machine learning in real environments

Wenjuan LI, Weizhi MENG

Research output: Chapter in Book/Report/Conference proceedingChapters

13 Citations (Scopus)

Abstract

Spam emails are considered as one of the biggest challenges for the Internet. Thus email classification, which aims to correctly classify legitimate and spam emails, becomes an important topic for both industry and academia. To achieve this goal, machine learning techniques, especially supervised machine learning algorithms, have been extensively applied to this field. In literature, several studies reveal that supervised machine learning (SML) suffers from some limitations such as performance fluctuation, hence many works start focusing on designing more complex algorithms. However, we identify that most existing research efforts are based on datasets, while more research should be conducted to investigate the performance of SML in real environments. In this paper, we thus perform an empirical study with three different environments and over 1,000 users regarding this issue. In the study, we find that SML classifiers like decision tree and SVMs are acceptable by users in real email classification. In addition, we discuss promising directions and provide new insights in this area. Copyright © 2015 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2015 IEEE International Conference on Communications, ICC 2015
Place of PublicationUSA
PublisherIEEE
Pages7438-7443
ISBN (Electronic)9781467364324
DOIs
Publication statusPublished - 2015

Citation

Li, W., & Meng, W. (2015). An empirical study on email classification using supervised machine learning in real environments. In Proceedings of 2015 IEEE International Conference on Communications, ICC 2015 (pp. 7438-7443). IEEE. https://doi.org/10.1109/ICC.2015.7249515

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