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Towards designing an email classification system using multi-view based semi-supervised learning

Li, Wenjuan, Meng, Weizhi, Tan, Zhiyuan and Xiang, Yang 2014, Towards designing an email classification system using multi-view based semi-supervised learning, in TrustCom 2014 : Proceedings of the IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, Piscataway, N.J., pp. 174-181, doi: 10.1109/TrustCom.2014.26.

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Title Towards designing an email classification system using multi-view based semi-supervised learning
Author(s) Li, Wenjuan
Meng, Weizhi
Tan, Zhiyuan
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Conference name IEEE International Trust, Security and Privacy in Computing and Communications Conference (13th : 2014 : Beijing, China)
Conference location Beijing, China
Conference dates 24-26 Sep. 2014
Title of proceedings TrustCom 2014 : Proceedings of the IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications
Publication date 2014
Start page 174
End page 181
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Email classification
Machine learning applications
Multi-view
Network security
Semi-supervised learning
Summary The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive task and requires in-depth domain knowledge. Thus, only a very small proportion of the data can be labeled in practice. This bottleneck greatly degrades the effectiveness of supervised email classification systems. In order to address this problem, in this work, we first identify some critical issues regarding supervised machine learning-based email classification. Then we propose an effective classification model based on multi-view disagreement-based semi-supervised learning. The motivation behind the attempt of using multi-view and semi-supervised learning is that multi-view can provide richer information for classification, which is often ignored by literature, and semi-supervised learning supplies with the capability of coping with labeled and unlabeled data. In the evaluation, we demonstrate that the multi-view data can improve the email classification than using a single view data, and that the proposed model working with our algorithm can achieve better performance as compared to the existing similar algorithms.
ISBN 9781479965137
ISSN 2324-898X
Language eng
DOI 10.1109/TrustCom.2014.26
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073551

Document type: Conference Paper
Collection: School of Information Technology
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