发表机构
San Jose State University（圣何塞州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现实数据集中的缺失数据问题，提出模式感知图神经网络，采用四种编码策略，在七个UCI数据集上实验，相比基线方法有显著改进，揭示了注意力机制在有模式信息时非关键，强调区分模式比特定任务优化更重要。
AI 中文摘要
缺失数据在现实世界数据集中普遍存在。传统方法要么丢弃不完整样本，要么采用忽略潜在信息缺失模式的插补技术，默认缺失是随机发生的。然而，缺失模式可能提供额外信息。我们提出模式感知图神经网络，它能在观测值旁明确编码哪些特征缺失。我们使用四种编码策略，在七个有自然缺失的UCI数据集上进行实验。我们的方法比基线有显著改进，所有数据集上平衡准确率平均提高17%，F1宏平均提高22%。不同数据集收益差异显著，注意力机制在有模式信息时并非关键。
英文摘要
Missing data is ubiquitous in real-world datasets. Traditional methods either discard incomplete samples or apply imputation techniques that ignore potentially informative missingness patterns, implicitly assuming that missingness occurs randomly. However, missingness patterns might provide additional information. We propose pattern-aware graph neural networks that explicitly encode which features are missing alongside observed values. We used four encoding strategies -- learned embeddings, frozen random embeddings, statistical features, and hierarchical representations -- across seven UCI datasets with naturally occurring missingness. Our Pattern-aware methods achieve substantial improvements over baselines, with an average improvement of 17\% in balanced accuracy and 22\% in F1-macro across all datasets. The benefits vary significantly by dataset: annealing shows dramatic improvement (+80\% balanced accuracy), while hepatitis and soybean show minimal gains (+4--5\%). Notably, even simple random pattern embeddings perform comparably to learned embeddings (0.650 vs 0.663 balanced accuracy), suggesting that distinguishing between patterns may be more important than task-specific optimization. Our ablation study reveals that attention mechanisms, while helpful, are not critical when pattern information is available -- simple mean aggregation with pattern awareness achieves 0.640 balanced accuracy compared to 0.645 for attention-based variants.
Comments2026 International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML), 20-22 March 2026
