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This paper presents a systematic machine learning study of 6G-IoT beamforming optimization, comparing network, environmental, device, and vision feature groups for predictive power, and applying clustering methods to enhance performance.
This paper systematically evaluates five imbalance handling methods (RUS, ROS, SMOTE, re-weighting, direct F1 optimization) on three biomedical datasets (tabular, text, image) using models of varying complexity. Results show that benefits depend on model complexity and data modality, with ROS, re-weighting, and direct F1 optimization being effective for complex models on unstructured data.