Deakin University

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

conference contribution
posted on 2023-10-26, 03:23 authored by W Li, W Meng, Z Tan, Yang Xiang
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.



174 - 181


Beijing, China

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Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2014, IEEE

Title of proceedings

TrustCom 2014 : Proceedings of the IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications