Internet users are always being attacked by spam messages, especially spam e-mails. Due to this issue, researchers had done many research works to find alternatives against the spam attacks. Different approaches, software and methods had been proposed in order to protect the Internet users from spam. This proposed work was inspired by the rough set theory, which was proven effective in handling uncertainties and large data set and also by the soft set theory which is a new emerging parameter reduction method that could overcome the limitation of rough set and fuzzy set theories in dealing with an uncertainty problem. The objective of this work was to propose a new hybrid parameter reduction method which could solve the uncertainty problem and inefficiency of parameterization tool issues which were used in the spam e-mail classification process. The experimental work had returned significant results which proved that the hybrid rough set and soft set parameter reduction method can be applied in the spam e-mail classification process that helps the classifier to classify spam e-mails effectively. As a recommendation, enhancement works on the functionality of this hybrid method shall be considered in different application fields, especially for the fields dealing with uncertainties problem and high dimension of data set.
Mohamad, M., & Selamat, A. (2016). A new hybrid rough set and soft set parameter reduction method for spam e-mail classification task. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9806 LNCS, pp. 18–30). Springer Verlag. https://doi.org/10.1007/978-3-319-42706-5_2