AINeutralarXiv – CS AI · 7h ago5/10
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Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing
Researchers propose a hybrid machine learning framework combining data-level and algorithm-level balancing techniques to address imbalanced regression problems, where underrepresented target values typically degrade model performance. The framework integrates adaptive partitioning, conditional variational autoencoders, strategic oversampling, and a novel weighted loss function to improve predictions on rare but important cases.