Comparative Analysis of Machine Learning Classifiers for E-mail Spam Filtering with RFE-SVM Feature Selection and SA-Optimised XGBoost

Authors

  • Abubakar HASSAN

    Author

  • Emmanuel G. DADA

    Author

DOI:
https://doi.org/10.33003/1n7mkc48
Keywords:
E-mail spam filtering, machine learning, XGBoost, Simulated Annealing optimization, RFE-SVM feature selection.
Abstract

This study presents a comparative evaluation of several machine learning classifiers for email spam filtering using a Kaggle dataset of 5,172 email samples. The investigation includes a proposed hybrid approach combining RFE-SVM feature selection with SA-optimized XGBoost, evaluated alongside SVM, MLP, Gaussian Naïve Bayes, and Logistic Regression. Results showed that MLP achieved the highest accuracy at 99.2%, followed by SVM at 99.0%, while the proposed SA-XGBoost achieved 98.3% with notably lower memory consumption of 0.20 MB. The findings suggest that for this dataset, simpler classifiers may outperform more complex ensemble methods, and that the proposed approach offers a favourable accuracy-memory trade-off for resource-constrained environments.

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Published
11-10-2026
Issue
Vol. 2 No. 2 (2026): December 2026
Section
Articles
License

Copyright (c) 2026 Abubakar HASSAN, Emmanuel G. DADA (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

[1]
A. HASSAN and E. G. DADA, “Comparative Analysis of Machine Learning Classifiers for E-mail Spam Filtering with RFE-SVM Feature Selection and SA-Optimised XGBoost”, FJET, vol. 2, no. 2, pp. 818–827, Oct. 2026, doi: 10.33003/1n7mkc48.

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