Real-time Spam Email Filtering with Stacking and Majority Voting Ensembles

Spam emails continue to be a major issue in digital communication, hurting productivity and jeopardizing security. This paper presents a real-time spam email filtering method that combines ensemble learning techniques (stacking and majority voting) with machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Decision Trees (DT). The objective is to improve the accuracy and reliability of spam detection systems. The proposed models were tested against Kaggle’s Email Spam Dataset on crucial performance parameters such as accuracy, precision, recall, F-measure, false positive rate (FPR), and false negative rate (FNR). The results show that, while the stacking ensemble is competitive, the majority voting ensemble consistently achieved good results. It delivers improved accuracy of 98.59%, precision of 98.59%, recall of 98.59%, and F-measure of 98.59% while keeping lower FPR of 0.019 and FNR of 0.009, making it the most dependable option for real-world use. This study demonstrates the efficiency of ensemble learning, particularly majority voting, in building scalable and practical solutions for real-time spam email filtering. The findings lay a solid foundation for enhancing email security mechanisms and user experience.