A Comparative Study of the Effectiveness of Selected Machine Learning Algorithms for Scalability and Accuracy in Filtering Email Spams
Fatimah Adamu-Fika, Blessing Ashedze, Kamaludeen Shehu Bature, Dauda Sule, Suleiman Abu Usman, Henry Onyeoma Mafua, Onyinye Vivian Okpoko · Advances in Multidisciplinary & Scientific Research Journal Publication · 2024
The rise in email spam necessitates effective filtering systems. This study examines three machine learning algorithms—Naive Bayes, Random Forest, and Support Vector Machines (SVM)—for spam filtering. Using a diverse dataset, we evaluated each model on accuracy, precision, recall, F1-score, and scalability. SVM outperformed others with a recall of 93.4% and an F1-score of 95.5%, making it suitable for high-security needs. Naive Bayes was efficient, achieving consistent accuracy of 98.3% regardless of dataset size. Random Forest excelled in handling noisy data, boasting a precision of 99.3%. The results emphasise the trade-offs between scalability and accuracy, aiding in the selection of algorithms based on specific requirements. This study connects theoretical advancements to practical applications and suggests ways to enhance spam filtering systems while considering ensemble methods for future research. Keywords: Email Spam Filtering, Machine Learning Algorithms, Naive Bayes, Random Forest, Support Vector Machines (SVM). Proceedings Citation Format Fatimah Adamu-Fika, Blessing Ashedze, Kamaludeen Shehu Bature, Dauda Sule, Suleiman Abu Usman, Henry Onyeoma Mafua, & Onyinye Vivian Okpoko (2024): A Comparative Study of the Effectiveness of Selected Machine Learning Algorithms for Scalability and Accuracy in Filtering Email Spams. Proceedings of the 38th iSTEAMS Multidisciplinary Bespoke Conference. 17th – 19th July, 2024. University of Ghana, Accra, Ghana. Pp 330-342. dx.doi.org/10.22624/AIMS/ACCRABESPOKE2024P33