Rebalancing with Calibrated Sub-classes (RCS): A Statistical Fusion-based Framework for Robust Imbalanced Classification across Modalities

• URL: http://arxiv.org/abs/2510.13656v2
• Date: Tue, 21 Oct 2025 19:14:41 GMT
• Title: Rebalancing with Calibrated Sub-classes (RCS): A Statistical Fusion-based Framework for Robust Imbalanced Classification across Modalities
• Authors: Priyobrata Mondal, Faizanuddin Ansari, Swagatam Das,
• Abstract summary: Rebalancing with Calibrated Sub-classes (RCS) is a novel distribution calibration framework for robust imbalanced classification.<n>RCS fuses statistical information from the majority and intermediate class distributions via a weighted mixture of Gaussian components.
• Score: 16.993547305381327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance, where certain classes have insufficient data, poses a critical challenge for robust classification, often biasing models toward majority classes. Distribution calibration offers a promising avenue to address this by estimating more accurate class distributions. In this work, we propose Rebalancing with Calibrated Sub-classes (RCS) - a novel distribution calibration framework for robust imbalanced classification. RCS aims to fuse statistical information from the majority and intermediate class distributions via a weighted mixture of Gaussian components to estimate minority class parameters more accurately. An encoder-decoder network is trained to preserve structural relationships in imbalanced datasets and prevent feature disentanglement. Post-training, encoder-extracted feature vectors are leveraged to generate synthetic samples guided by the calibrated distributions. This fusion-based calibration effectively mitigates overgeneralization by incorporating neighborhood distribution information rather than relying solely on majority-class statistics. Extensive experiments on diverse image, text, and tabular datasets demonstrate that RCS consistently outperforms several baseline and state-of-the-art methods, highlighting its effectiveness and broad applicability in addressing real-world imbalanced classification challenges.

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