用模糊图注意力与Transformer联合补全无线数据，提升缺失场景下的准确性。
FGATT: A Robust Framework for Wireless Data Imputation Using Fuzzy Graph Attention Networks and Transformer Encoders
- 结合模糊图注意力与Transformer，动态捕捉时空依赖关系。
- 在高缺失率下仍保持高补全精度，优于现有方法。
- 适合无线传感网和物联网等对数据完整性要求高的场景。
无线网络中缺失数据是普遍存在的挑战，常导致机器学习与深度学习模型性能下降。为此，我们提出一种新框架FGATT，将模糊图注意力网络（FGAT）与Transformer编码器结合，实现鲁棒且准确的数据补全。FGAT利用模糊粗糙集与图注意力机制，即使在缺乏预定义空间信息的情况下也能动态捕捉空间依赖。Transformer编码器通过自注意力机制建模时间依赖，聚焦关键时序模式。引入自适应图构建方法，实现动态连接学习，提升框架在多种无线数据集上的适用性。大量实验表明，该方法在补全精度与鲁棒性上均优于当前先进方法，尤其在缺失数据比例较高时表现更优。所提模型适用于无线传感器网络与物联网环境，其中数据完整性至关重要。
原文摘要 · Abstract (English)
Missing data is a pervasive challenge in wireless networks and many other domains, often compromising the performance of machine learning and deep learning models. To address this, we propose a novel framework, FGATT, that combines the Fuzzy Graph Attention Network (FGAT) with the Transformer encoder to perform robust and accurate data imputation. FGAT leverages fuzzy rough sets and graph attention mechanisms to capture spatial dependencies dynamically, even in scenarios where predefined spatial information is unavailable. The Transformer encoder is employed to model temporal dependencies, utilizing its self-attention mechanism to focus on significant time-series patterns. A self-adaptive graph construction method is introduced to enable dynamic connectivity learning, ensuring the framework's applicability to a wide range of wireless datasets. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in imputation accuracy and robustness, particularly in scenarios with substantial missing data. The proposed model is well-suited for applications in wireless sensor networks and IoT environments, where data integrity is critical.
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