GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning

• URL: http://arxiv.org/abs/2412.14561v1
• Date: Thu, 19 Dec 2024 06:26:16 GMT
• Title: GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
• Authors: Jintao Huang, Yiu-ming Cheung, Chi-man Vong, Wenbin Qian,
• Abstract summary: We introduce Granular Ball Representation for Imbalanced (GBRIP), a novel framework for imbalanced learning.<n>GBRIP utilizes coarse-grained granular ball representation and multi-center loss to construct a granular ball-based nfeature space through unsupervised learning.<n>The novel multi-center loss function enhances learning by emphasizing the relationships between samples and their respective centers within the granular balls.
• Score: 34.555684625956516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo-labeling from inter-class features, often overlooking the significant impact of the intra-class imbalanced features combined with the inter-class. To address these limitations, we introduce Granular Ball Representation for Imbalanced PLL (GBRIP), a novel framework for imbalanced PLL. GBRIP utilizes coarse-grained granular ball representation and multi-center loss to construct a granular ball-based nfeature space through unsupervised learning, effectively capturing the feature distribution within each class. GBRIP mitigates the impact of confusing features by systematically refining label disambiguation and estimating imbalance distributions. The novel multi-center loss function enhances learning by emphasizing the relationships between samples and their respective centers within the granular balls. Extensive experiments on standard benchmarks demonstrate that GBRIP outperforms existing state-of-the-art methods, offering a robust solution to the challenges of imbalanced PLL.

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