Ensemble Learning-Based Approach for Email Spam Detection

Email spam remains a pervasive and escalating challenge that poses serious inconveniences, security vulnerabilities, and productivity obstacles for global users. Hence, it is crucially imperative to create more precise and efficient spam detection models for email platforms. Many studies in the literature leverage traditional machine learning techniques for email spam detection. However, despite their simplicity, these methods still face shortcomings in accuracy and generalization. This paper proposed an effective Ensemble Learning model for detecting email spam using Support Vector Classifier, Multilayer Perceptron, and Extra Tree Classifier. Diverse metrics are employed to evaluate the performance of the developed spam email detection system. Experimental results underscore the effectiveness of the proposed model in accurately detecting and classifying spam emails surpassing conventional classification methods. The deployed model exhibits elevated detection rates while sustaining a minimal false positive rate, thereby enhancing the overall user experience and fortifying security measures.