TF-IDF and Ensemble Learning for Enhanced Spam Guard A Robust Approach with Drift Adaptation

DOI

10.2991/978-94-6463-718-2_149How to use a DOI?

Keywords

Spam detection; TF-IDF; ensemble learning; concept drift adaptation; real-time spam filtering; multilingual spam; multimodal datasets; performance metrics; scalability; computational efficiency

Abstract

Spam detection continues to be an important problem in the changing internet ecosystem, especially with the rise of advanced spam techniques and the adaptive changes in spam content. We propose a novel framework for spam detection that leverages the benefits of TF-IDF to efficiently extract features in combination with the advances developed in ensemble learning methods to achieve improved accuracy in classification tasks. Our proposed solution takes this step further by accounting for drift in the underlying concepts using a dynamic drift adaptation strategy, enabling the system to maintain high performance over time, even as spam systems evolve. Evaluations on multilingual and multimodal spam show its scalability and resilience across platforms. In addition to this, the proposed system adopts several performance metrics when evaluating the proposed system, notably precision, recall, F1-score, and even computational efficiency, so that the proposed system can be implemented in low-resource conditions. There is a clear need for the kind of scalable, adaptive and efficient solution that this work primarily introduces, given the issues present in existing spam detection methodologies.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Banupriya
AU  - S. Vadivel
AU  - S. Sadhasivam
AU  - V. K. Devaprasath
AU  - S. Deepak
AU  - M. Karthikeyan
PY  - 2025
DA  - 2025/05/23
TI  - TF-IDF and Ensemble Learning for Enhanced Spam Guard A Robust Approach with Drift Adaptation
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1804
EP  - 1815
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_149
DO  - 10.2991/978-94-6463-718-2_149
ID  - Banupriya2025
ER  -