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September 30, 2025ElectronicsOpen Access

A Comparative Evaluation of a Multimodal Approach for Spam Email Classification Using DistilBERT and Structural Features

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Evaluation shows that a multimodal approach improves spam detection using deep learning and machine learning techniques.

A multimodal architecture combining DistilBERT and structural features achieved a 99.62% test score.
Random Forest was identified as the most effective classical machine learning method in spam classification.
The study highlights the challenge of concept drift, indicating that models showed performance degradation on modern spam.
DistilBERT demonstrated better robustness against temporal decay compared to traditional spam detection systems.

Asliyuksek et al. (2025) studied this question.

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September 30, 2025ElectronicsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Evaluation shows that a multimodal approach improves spam detection using deep learning and machine learning techniques.

A multimodal architecture combining DistilBERT and structural features achieved a 99.62% test score.
Random Forest was identified as the most effective classical machine learning method in spam classification.
The study highlights the challenge of concept drift, indicating that models showed performance degradation on modern spam.
DistilBERT demonstrated better robustness against temporal decay compared to traditional spam detection systems.

Asliyuksek et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark