用图注意力网络选关键数据，大幅减量仍保精度。
GAIS: A Novel Approach to Instance Selection with Graph Attention Networks
- 构建数据图，用GAT学习实例间关系
- 平均减少96%数据，模型性能不降反升
- 适合需要高效数据筛选的机器学习任务
实例选择（IS）是机器学习中通过减少数据集规模来提升效率的关键技术。本文提出一种基于图注意力网络（GAIS）的新方法，将数据表示为图结构，利用GAT学习节点表示，捕捉实例间的复杂关联。该方法分块处理数据，在图构建中引入随机掩码和相似性阈值，并根据训练后GAT模型的置信度分数选择最具信息量的实例。在13个不同数据集上的实验表明，GAIS在有效性上持续优于传统方法，平均实现96%的数据缩减率，同时保持或提升模型性能。尽管计算成本略高，但其在显著减少训练数据的同时维持高准确性的优势，使其成为图结构数据选择的有力方案。
原文摘要 · Abstract (English)
Instance selection (IS) is a crucial technique in machine learning that aims to reduce dataset size while maintaining model performance. This paper introduces a novel method called Graph Attention-based Instance Selection (GAIS), which leverages Graph Attention Networks (GATs) to identify the most informative instances in a dataset. GAIS represents the data as a graph and uses GATs to learn node representations, enabling it to capture complex relationships between instances. The method processes data in chunks, applies random masking and similarity thresholding during graph construction, and selects instances based on confidence scores from the trained GAT model. Experiments on 13 diverse datasets demonstrate that GAIS consistently outperforms traditional IS methods in terms of effectiveness, achieving high reduction rates (average 96\%) while maintaining or improving model performance. Although GAIS exhibits slightly higher computational costs, its superior performance in maintaining accuracy with significantly reduced training data makes it a promising approach for graph-based data selection.
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