Towards designing an email classification system using multi-view based semi-supervised learning Li, W, Meng, W, Tan, Z 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. Attached Files Name Description MIMEType Size Downloads Title Towards designing an email classification system using multi-view based semi-supervised learning Author(s) Li, W Meng, W Tan, Z 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 2014/09/24 - 2014/09/26 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 Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering SUPPORT VECTOR MACHINES SPAM DETECTION CLASSIFIERS 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:30082891 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 18 Apr 2016, 10:40:51 EST Mon, 18 Apr 2016, 10:43:57 EST Mon, 18 Apr 2016, 10:44:52 EST Mon, 18 Apr 2016, 10:48:12 EST Mon, 18 Apr 2016, 12:45:47 EST Fri, 09 Mar 2018, 13:29:10 EST Fri, 09 Mar 2018, 13:51:01 EST Fri, 09 Mar 2018, 14:11:28 EST Mon, 12 Mar 2018, 07:58:46 EST Mon, 12 Mar 2018, 08:00:06 EST Mon, 12 Mar 2018, 08:02:19 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 10 times in Scopus Search Google Scholar Access Statistics: 58 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Fri, 09 Mar 2018, 13:29:10 EST

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