An E-mail Spam Detection using Stacking and Voting Classification Methodologies
Автор: Aasha Singh, Awadhesh Kumar, Ajay Kumar Bharti, Vaishali Singh
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 6 vol.14, 2022 года.
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Nowadays, we use emails almost in every field; there is not a single day, hour, or minute when emails are not used by people worldwide. Emails can be categorized into two types: ham and spam. Hams are useful emails, while spam is junk or unwanted emails. Spam emails may carry some unwanted, harmful information or viruses with them, which might harm user privacy. Spam mails are used to harm people by wasting their time and energy and stealing valuable information. Due to increasing in spam emails rapidly, spam detection and filtering are the prominent problems that need to be solved. This paper discusses various machine learning models like Naïve Bayes, Support Vector Machine, Decision Tree, Extra Decision Tree, Linear regression., and surveys about these machine learning techniques for email spam detection in terms of their accuracy and precision. In this paper, a comprehensive comparison of these techniques and stacking of different algorithms is also made based on their speed, accuracy, and precision performance.
Email, Spam, SVM, Linear Regression, Stacking, Voting
Короткий адрес: https://sciup.org/15018794
IDR: 15018794 | DOI: 10.5815/ijieeb.2022.06.03
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