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EvoSampling: A Granular Ball-Based Evolutionary Hybrid Sampling With Knowledge Transfer for Imbalanced Learning [Research Frontier]
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AI
裴
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B
张
Y
S
DOI：10.1109/MCI.2026.3657694.png)
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Abstract
En 中文
Class imbalance tends to result in biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, the minority class is usually of crucial importance in many real-life applications. Hybrid sampling methods address this issue by oversampling the minority class to increase the number of its instances, followed by undersampling to remove low-quality instances. However, most existing sampling methods face difficulties in generating diverse high-quality instances and often fail to remove noise or low-quality instances on a larger scale effectively. Therefore, this paper proposes an evolutionary multi-granularity hybrid sampling method, called EvoSampling, to tackle this hindrance. During the oversampling process, genetic programming is used with multi-task learning to effectively and efficiently generate diverse high-quality instances. During the undersampling process, a granular ball-based undersampling method is employed to remove noise in a multi-granular fashion, thereby enhancing data quality. Experiments on 18 imbalanced datasets demonstrate that EvoSampling effectively enhances the performance of various classification algorithms by providing better datasets than existing sampling methods. Ablation studies further indicate that allowing knowledge transfer accelerates the evolutionary learning process.
Keywords:
Classification algorithms
Sampling methods
Hybrid learning
Genetic programming
Multitasking
Noise measurement
Data models
Knowledge transfer
Data integrity
Accuracy
Granular computing
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11.2
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