Bias-Corrected Data Synthesis for Imbalanced Learning
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 naïvely 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Task LearningCausal InferenceSimilar Papers 제목 키워드 기반
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep model performance. Recent studies tend to regard low-loss samples…
Learning with noisy labelsBayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a ma…
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
Diffusion models have made significant advances recently in high-quality image synthesis and related tasks. However, diffusion models trained on real-world datasets, which often follow long-tailed distributions, yield in…
Contrastive LearningImage GenerationmodelGraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification
In many real-world node classification scenarios, nodes are highly class-imbalanced, where graph neural networks (GNNs) can be readily biased to major class instances. Albeit existing class imbalance approaches in other …
BlockingClassificationNode ClassificationPredicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is c…
Machine TranslationRetrosynthesisTranslation