Meta-learner-based frameworks for interpretable email spam detection
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.
Authors
- Meghana Kshirsagar (ORCID: https://orcid.org/0000-0002-8182-2465)
- Conor Ryan (ORCID: https://orcid.org/0000-0002-7002-5815)
- Vedant Rathi
Institutions
- University of Illinois Urbana-Champaign (US)
- Science Foundation Ireland (IE)
- University of Limerick (IE)
- Lero (IE)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-10-21
- DOI
- https://doi.org/10.3389/frai.2025.1569804
- Primary Topic
- Spam and Phishing Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00