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Spam email classification based on SVM, Transformer and Naive Bayes
- 1 Harbin Institute of Technology (Weihai)
* Author to whom correspondence should be addressed.
Abstract
As a matter of fact, with the booming of information and big data, there are too many unwanted e-mails called “spam” sent to people’s e-mail account in recent years. On this basis, it could lead to a lot of problems including occupying the public resources, causing financial loss and so on. With this in mind, spam filtering technique is in need to solve the problem and address the issues. In reality, based on previous analysis, machine learning methods are very effective in spam filtering. On this basis, this study carried out background research of machine learning algorithms in spam filtering, and find the spam e-mail dataset of Kaggle.com, and implement 3 algorithms on the dataset. According to the analysis, Transformer and SVM work better on the dataset, and SVM is the best. At the same time, the current limitations are discussed as well. In addition, the prospects are demonstrated in the meantime.
Keywords
Spam email, machine learning, Naive Bayes, Transformer, SVM
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Cite this article
Qiao,Y. (2024). Spam email classification based on SVM, Transformer and Naive Bayes. Applied and Computational Engineering,48,161-167.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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