ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1569804
Meta-Learner-Based Frameworks for Interpretable Email Spam Detection
Provisionally accepted- 1University of Limerick, Limerick, Ireland
- 2University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Introduction: With the increasing reliance on digital communication, email has become an essential tool for personal and professional correspondence. However, despite its numerous benefits, digital communication faces significant challenges, particularly the prevalence of spam emails. Effective spam email classification systems are crucial to mitigate these issues by automatically identifying and filtering out unwanted messages, enhancing the efficiency of email communication. Methods: We compare five traditional machine-learning and five deep-learning spam classifiers against a novel meta-learner, evaluating how different word embeddings, vectorization schemes, and model architectures affect performance on the Enron-Spam and TREC 2007 datasets. The primary aim is to show how the meta-learner's combined predictions stack up against individual ML and DL approaches. Results: Our meta-learner outperforms all state-of-the-art models, achieving an accuracy of 0.9905 and an AUC score of 0.9991 on a hybrid dataset that combines Enron-Spam and TREC 2007. To the best of our knowledge, our model also surpasses the only other meta-learning-based spam detection model reported in recent literature, with higher accuracy, better generalization from a significantly larger dataset, and lower computational complexity. We also evaluated our meta-learner in a zero-shot setting on an unseen real-world dataset, achieving a spam sensitivity rate of 0.8970 and an AUC score of 0.7605. Discussion: These results demonstrate that meta-learning can yield more robust, bias-resistant spam filters suited for real-world deployment. By combining complementary model strengths, the meta-learner also offers improved resilience against evolving spam tactics.
Keywords: machine learning, deep learning, Spam Email Detection, Natural Language Processing, Classification, Meta-learner, algorithmicbias, Data bias
Received: 01 Feb 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Kshirsagar, Rathi and Ryan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Meghana Kshirsagar, meghana.kshirsagar@ul.ie
Conor Ryan, conor.ryan@ul.ie
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.