Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise

• URL: http://arxiv.org/abs/2312.07087v1
• Date: Tue, 12 Dec 2023 09:09:45 GMT
• Title: Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
• Authors: Hwanjun Song and Minseok Kim and Jae-Gil Lee
• Abstract summary: 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.
• Score: 31.917931364881625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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