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Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing
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DOI：10.1016/j.eswa.2026.131908.png)
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Abstract
En 中文
• Novel hybrid framework integrating data- and algorithm-level imbalanced learning • Novel adaptive bin partitioning to segment target spaces based on local patterns • Novel data-level balancing via feature clustering and oversampling in sparse regions • Novel latent-density weighted loss to prioritize rare samples in target spaces • Superior performance on benchmark datasets
Keywords:
Imbalanced Regression
Adaptive Bin Partitioning
Feature Clustering
Latent-Density Weighted Loss
Data-Level Balancing
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