In comparison of deep learning models, classical machine learning (ML) classifiers offer distinct advantages such as robust interpretability and lightweight models, but are limited by their reliance on human-engineered features.
By employing ClassBD as a feature extractor, we observed significant performance improvements in traditional ML classifiers, demonstrating its value in enhancing the capabilities of these shallower models.
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