Bias-Corrected Data Synthesis for Imbalanced Learning

2025-10-29

AI Summary

In class-imbalanced classification, synthesizing minority-class samples often introduces distributional bias, leading to model overfitting and degraded generalization. To address this, we propose a novel framework that estimates and corrects synthesis-induced bias using distributional information from the majority class. Unlike conventional approaches assuming synthesized samples follow the true minority-class distribution, our method leverages structural consistency in majority-class features to construct a provably consistent bias estimator, coupled with dynamic error calibration during training. Theoretically, we derive bounds on the bias estimation error and provide guarantees on improved prediction accuracy. Empirically, extensive experiments on benchmark datasets—including MNIST—demonstrate significant gains in F1-score, AUC, and robustness against label noise. Moreover, the framework naturally extends to multi-task learning and causal inference settings, offering broad applicability without architectural modification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Imbalanced data, where the positive samples represent only a small proportion compared to the negative samples, makes it challenging for classification problems to balance the false positive and false negative rates. A common approach to addressing the challenge involves generating synthetic data for the minority group and then training classification models with both observed and synthetic data. However, since the synthetic data depends on the observed data and fails to replicate the original data distribution accurately, prediction accuracy is reduced when the synthetic data is naively treated as the true data. In this paper, we address the bias introduced by synthetic data and provide consistent estimators for this bias by borrowing information from the majority group. We propose a bias correction procedure to mitigate the adverse effects of synthetic data, enhancing prediction accuracy while avoiding overfitting. This procedure is extended to broader scenarios with imbalanced data, such as imbalanced multi-task learning and causal inference. Theoretical properties, including bounds on bias estimation errors and improvements in prediction accuracy, are provided. Simulation results and data analysis on handwritten digit datasets demonstrate the effectiveness of our method.

Problem

• Correcting bias in synthetic data for imbalanced classification problems
• Improving prediction accuracy by mitigating synthetic data adverse effects
• Extending bias correction to multi-task learning and causal inference

Innovation

• Bias correction procedure for synthetic data
• Consistent bias estimators from majority group
• Extends to multi-task learning and causal inference