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Contribution-based imbalanced hybrid resampling ensemble
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DOI：10.1016/j.patcog.2025.111553.png)
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Abstract
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Resampling is an effective method for addressing data imbalance. Prevailing methods adjust the data distribution by either describing information or noise, and exhibit superiority in many scenarios. However, current studies face challenges in considering both information and noise simultaneously, as noisy samples usually have high information levels, potentially leading to misestimation. In this paper, a Contribution-Based Hybrid Resampling Ensemble (CHRE) is proposed to address the correlation problem between information and noise. CHRE is a semi-supervised algorithm based on a novel Global Unified Data Evaluation (GUDE) framework. Firstly, GUDE describes sample contribution by redefining the information and noise levels. Subsequently, based on sample contribution, CHRE removes negatively contributing majority samples, and oversamples minority samples Concurrently, pseudo-labels related to these minority samples are included in the oversampling. Throughout this process, CHRE resamples based on the sample contribution and optimizes the model. GUDE provides sample contribution based on the model feedback, with both interacting for iterative optimization. Extensive experiments are conducted on 53 benchmark datasets, involving three base classifiers and 13 state-of-the-art imbalance algorithms. The results demonstrate significant advantages of CHRE. Noise studies further indicate the high robustness of CHRE.
Keywords:
Class-imbalanced learning
Hybrid resampling
Sample contribution
Ensemble learning
Semi-supervised Learning
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