Development of an Ensemble Model for Email Filtering and Classification

Authors

  • Bolaji A. OMODUNBI

    Author

  • Hammed A. OLASUNKANMI

    Author

DOI:
https://doi.org/10.33003/vt48zv20
Keywords:
Ensemble Learning, Email Classification, Spam Detection, Machine Learning, BERT.
Abstract

This research presents an advanced ensemble-driven framework for email classification that integrates conventional machine learning algorithms with deep transfer learning methods. A well-structured dataset comprising spam, phishing, and legitimate email samples was assembled and processed using both TF-IDF representations and contextual embeddings derived from BERT. The proposed approach combines Naïve Bayes, Support Vector Machine, and BERT models through a weighted voting strategy, with weights fine-tuned via cross-validation. Experimental evaluation based on performance metrics such as accuracy, precision, recall, and F1-score indicates notable effectiveness, with the model achieving 98% accuracy, 97% precision, 98% recall, and 98% F1-score. Furthermore, the system demonstrated a high capability in identifying phishing emails while minimizing false negative rates. These results highlight the advantage of integrating traditional machine learning techniques with deep learning models to achieve a more reliable and efficient email classification system.

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Published
25-04-2026
Issue
Vol. 2 No. 1 (2026): June 2026
Section
Articles
License

Copyright (c) 2026 FUDMA Journal of Engineering and Technology

Creative Commons License

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

How to Cite

[1]
Bolaji A. OMODUNBI and Hammed A. OLASUNKANMI, “Development of an Ensemble Model for Email Filtering and Classification”, FJET, vol. 2, no. 1, pp. 490–499, Apr. 2026, doi: 10.33003/vt48zv20.

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