Effective Email Spam Detection System using Extreme Gradient Boosting

TL;DR

This paper introduces an improved email spam detection system using Extreme Gradient Boosting (XGBoost), demonstrating superior performance over previous models in accurately identifying spam emails.

Contribution

The study applies XGBoost to spam detection, showing its effectiveness and providing a comparative analysis with existing approaches, which is relatively underexplored in this context.

Findings

XGBoost-based model outperforms previous spam detection methods

The proposed model achieves higher accuracy and precision

Analysis confirms the robustness of the XGBoost approach

Abstract

The popularity, cost-effectiveness and ease of information exchange that electronic mails offer to electronic device users has been plagued with the rising number of unsolicited or spam emails. Driven by the need to protect email users from this growing menace, research in spam email filtering/detection systems has being increasingly active in the last decade. However, the adaptive nature of spam emails has often rendered most of these systems ineffective. While several spam detection models have been reported in literature, the reported performance on an out of sample test data shows the room for more improvement. Presented in this research is an improved spam detection model based on Extreme Gradient Boosting (XGBoost) which to the best of our knowledge has received little attention spam email detection problems. Experimental results show that the proposed model outperforms earlier approaches across a wide range of evaluation metrics. A thorough analysis of the model results in comparison to the results of earlier works is also presented.