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Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
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Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two samplers for imbalanced labels, generating minority-augmented instances with high diversity. It also refines multi-labels at the label-wise granularity, categorizing noisy labels as clean, re-labeled, or ambiguous for robust optimization. Extensive experiments on three benchmark datasets demonstrate that BalanceMix outperforms existing state-of-the-art methods. We release the code at https://github.com/DISL-Lab/BalanceMix.
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Sampling Imbalanced Data with Multi-objective Bilevel Optimization
A heuristic bilevel optimization wrapper around SVM-SMOTE is claimed to improve minority-class F1 by selecting training samples that increase model-output variance and reduce overlap.
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