通过进化生成与分粒度去噪，提升少数类样本质量
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning
- 用遗传编程+多任务学习生成多样高质量少数类样本
- 分粒度球形方法有效去除噪声，提升数据整体质量
- 支持知识迁移，加速进化过程，适合不平衡数据场景
类别不平衡会导致分类器偏向多数类而损害少数类性能。然而，在许多实际应用中，少数类尤为重要。混合采样方法通过先对少数类过采样增加实例数，再通过欠采样移除低质量样本以缓解此问题。但现有方法在生成多样化高质量样本方面存在困难，且难以大规模有效去除噪声或低质样本。为此，本文提出一种基于进化的多粒度混合采样方法——EvoSampling。在过采样阶段，采用遗传编程（GP）结合多任务学习，高效生成多样且高质量的少数类样本；在欠采样阶段，设计一种分粒度球形方法，以多粒度方式剔除噪声，显著提升数据质量。在20个不平衡数据集上的实验表明，EvoSampling能有效提升多种分类算法性能，生成的数据集优于现有方法。消融实验进一步表明，引入知识迁移可加速GP的进化学习过程。
原文摘要 · Abstract (English)
Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class is of importance in many real-life applications. Hybrid sampling methods address this 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. This paper therefore proposes an evolutionary multi-granularity hybrid sampling method, called EvoSampling. During the oversampling process, genetic programming (GP) is used with multi-task learning to effectively and efficiently generate diverse high-quality instances. During the undersampling process, we develop a granular ball-based undersampling method that removes noise in a multi-granular fashion, thereby enhancing data quality. Experiments on 20 imbalanced datasets demonstrate that EvoSampling effectively enhances the performance of various classification algorithms by providing better datasets than existing sampling methods. Besides, ablation studies further indicate that allowing knowledge transfer accelerates the GP's evolutionary learning process.
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