A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computer Science

Background:

• Real-world datasets often exhibit class imbalance, biasing Empirical Risk Minimization (ERM) towards majority classes.
• Existing loss modification methods (re-weighting, logit-adjustment) lack fine-grained analysis, failing to fully explain empirical outcomes.
• Current analyses use global properties, inadequately capturing the influence of class-dependent terms on learning dynamics.

Purpose of the Study:

• To develop a unified perspective for improving and adjusting loss-oriented methods in machine learning.
• To address the limitations of global property analysis in understanding class-imbalanced learning.
• To propose a principled learning algorithm that enhances generalization to minority classes.

Main Methods:

• Exploration of localized versions of properties, defined within each class, to analyze learning dynamics.
• Application of localized calibration for consistency validation across diverse loss functions.
• Utilization of localized Lipschitz continuity for deriving fine-grained generalization bounds.

Main Results:

• A unified theoretical framework for understanding and improving loss-oriented methods for imbalanced data.
• Development of a novel learning algorithm grounded in localized analysis.
• Empirical validation of the theoretical findings and algorithm effectiveness on ResNets and foundation models.

Conclusions:

• Localized properties provide a more effective lens for analyzing and refining class-imbalanced learning strategies.
• The proposed principled learning algorithm demonstrates significant improvements in generalization, particularly for minority classes.
• The findings offer a cohesive approach to addressing class imbalance in machine learning.