Research Output

Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning

  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.

  • Date:

    30 September 2014

  • Publication Status:


  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science


Li, W., Meng, W., Tan, Z., & Xiang, Y. (2014). Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning. In 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications,, (174-181).



Electronic mail, Semisupervised learning, Training, Supervised learning, Data models, Support vector machines, Feature extraction

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