.png){kind=icon}
.png){kind=icon}
Return
Meta-learner-based frameworks for interpretable email spam detection
.png){kind=icon}
OA
AI
DOI：10.3389/frai.2025.1569804.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
IntroductionWith 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.MethodsWe 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.ResultsOur 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.DiscussionThese 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.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
4.7
Papers:
2.4K
Citations:
4.4K