用图注意力引导随机游走，生成更精准的少数类样本
GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification
- 用图注意力网络指导随机游走，聚焦关键邻居区域
- 生成的新样本有效扩展类别边界，提升分类准确率
- 适合处理数据不平衡问题的机器学习场景
类别不平衡是机器学习中的重大挑战，常导致模型偏向多数类。本文提出GAT-RWOS，一种基于图的新型过采样方法，结合图注意力网络（GAT）与基于随机游走的过采样优势。GAT-RWOS利用GAT的注意力机制引导随机游走过程，重点关注每个少数类节点的最相关信息邻域。通过执行注意力引导的随机游走，并沿遍历路径插值特征，GAT-RWOS生成合成少数类样本，既能扩展类别边界，又保持原始数据分布。在一系列不平衡数据集上的大量实验表明，GAT-RWOS在提升分类性能方面优于当前最优过采样技术。该方法有望显著提升机器学习模型在不平衡数据上的表现，推动更可靠的分类系统发展。
原文摘要 · Abstract (English)
Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combines the strengths of Graph Attention Networks (GATs) and random walk-based oversampling. GAT-RWOS leverages the attention mechanism of GATs to guide the random walk process, focusing on the most informative neighbourhoods for each minority node. By performing attention-guided random walks and interpolating features along the traversed paths, GAT-RWOS generates synthetic minority samples that expand class boundaries while preserving the original data distribution. Extensive experiments on a diverse set of imbalanced datasets demonstrate the effectiveness of GAT-RWOS in improving classification performance, outperforming state-of-the-art oversampling techniques. The proposed method has the potential to significantly improve the performance of ML models on imbalanced datasets and contribute to the development of more reliable classification systems.
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