DEGL: dual-entropy guided loss with dynamic temperature regularization for long-tailed and imbalance-prone medical image classification

Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels

Bounded predictive influence and reliability-guided geometry as complementary mechanisms for imbalanced learning with uncertain labels are supported as complementary mechanisms for imbalanced learning with uncertain labels.

Semi-supervised weighted stacked autoencoder with spectral peak significance constraints for imbalanced fault diagnosis under low labeled rates

A semi-supervised weighted stacked autoencoder with spectral peak significance constraints (SSWAEF), a nearest-neighbor consistency voting strategy coupled with a reciprocal-class-size sampling mechanism is first developed to construct a high-quality, class-balanced pseudo-labeled dataset.

Machine Learning with Applications

A Gradient-Level Diagnosis of Extreme Class Imbalance in Multiple Instance Learning via q-Calculus

This work identifies ratio-invariant safety—non-degradation at any imbalance ratio, satisfied by vanilla BCE and q-calculus but violated by all reweighting methods—and gives recommendations spanning moderate to extreme imbalance.

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance

SoftMCC is a calibration-sensitive MCC-family selector with bounded stability and utility evidence, coupling an MCC-specific calibrated identity with a tie-aware, shared-pool selection protocol.

Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation

Factorized ablations further validate the complementary roles of CBA and DPA, supporting balanced prompt adaptation as an effective alternative to entropy minimization for test-time binary segmentation.