Enhancing email classification using data reduction and disagreement-based semi-supervised learning

Yuxin MENG, Wenjuan LI, Lam-For KWOK

Research output: Chapter in Book/Report/Conference proceedingChapters

18 Citations (Scopus)

Abstract

Email classification is an important topic in literature attempting to correctly classify user emails and filter out spam emails. In this paper, we identify some challenges regarding this topic and propose an effective email classification model based on both data reduction and disagreement-based semi-supervised learning. In particular, the main objective of the data reduction is to select an optimum collection of email features and reduce the pointless data, while the objective of the disagreement-based approach is to enhance the accuracy of detecting spam emails by utilizing unlabeled data automatically. In the evaluation, we explore the performance of our proposed email classification model using two public datasets and a private dataset. The experimental results demonstrate that our proposed model can overall enhance the performance of email classification through improving detection accuracy and reducing false rates. Copyright © 2014 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2014 IEEE International Conference on Communications, ICC 2014
Place of PublicationUSA
PublisherIEEE
Pages622-627
ISBN (Print)9781479920037
DOIs
Publication statusPublished - 2014

Citation

Meng, Y., Li, W., & Kwok, L.-F. (2014). Enhancing email classification using data reduction and disagreement-based semi-supervised learning. In Proceedings of 2014 IEEE International Conference on Communications, ICC 2014 (pp. 622-627). IEEE. https://doi.org/10.1109/ICC.2014.6883388

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