Spam Email Classification by Hybrid Feature Selection with Advanced Machine learning Algorithm – Future Perspective - Vivekanandam, B.; Balaganesh (Journal of Soft Computing Paradigm, 2022)
Recently, email has become a common way for people to communicate and share information both officially and personally. Email may be used by spammers to transmit harmful materials to Internet users. The data must be protected from unauthorized access, which necessitates the development of a reliable method for identifying spam emails. As a result, a variety of solutions have been devised. An innovative hybrid machine learning strategy for effectively detecting spam emails has been discussed in this study. This means that identifying spam and non-spam email is a difficult process. Spam email categorization has undergone a significant evolution in recent years, as shown by the research given below. For locating spam, this study uses a mixed approach. Different email categorization algorithms are used to rank them for future perspective.
Keywords: Hybrid Neural Networks; email spam classification; Feature selection; artificial bee colony; ant colony optimization