A meta-learning framework for handling class imbalance in supervised learning tasks. Leverages episodic training and adaptive sampling to boost minority class performance across diverse domains.
-
Updated
Jul 20, 2025 - Python
A meta-learning framework for handling class imbalance in supervised learning tasks. Leverages episodic training and adaptive sampling to boost minority class performance across diverse domains.
Research exploring how hyperparameter tuning, class-specific data augmentation, and MentalBERT's domain-specific architecture each contribute to improving multi-class mental health text classification and minority class detection.
Add a description, image, and links to the minority-class-detection topic page so that developers can more easily learn about it.
To associate your repository with the minority-class-detection topic, visit your repo's landing page and select "manage topics."