TailBooster：具备操作有效性约束的极值增强双层生成框架
TailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement
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中文总结 AI 辅助
TailBooster是结合生成建模与双异常检测层的双层框架，用于解决混合表格数据的极值增强问题，可提升极端事件预测的操作有效性与精度，且与模型无关可扩展至多领域。
中文摘要 AI 辅助
航空运输中的极端事件，如严重到达延误和异常空中时间，会引发级联网络中断，带来巨大的运营、经济和安全成本。此类事件在历史记录中较为罕见，导致机器学习模型缺乏足够的训练信号。合成数据增强提供了一种原则性解决方案，但传统生成模型对分布尾部的代表性不足，且无法保证生成的实例符合操作可行性，例如空中时间短但飞行距离长的情况。目前尚无现有方法针对混合类型表格记录同时解决这两个局限性。我们提出TailBooster，一种结合生成建模与两个异常检测层的双层生成框架。统计层通过四分位距提取极值，为专用生成模型（此处为表格变分自编码器）提供集中于尾部的训练信号。深度学习层随后应用基于自编码器的清洗，丢弃违反从历史数据学习到的操作范围的合成记录。该框架在五个维度上对美国航班记录进行了评估：多样性、统计相似性、保真度、操作有效性和效用，其中后两者是主要改进目标。数据驱动的清洗显著提高了操作有效性，而针对性增强提升了极端事件预测的效用。在六种回归算法中，与传统合成数据相比，使用该框架生成的记录进行训练，极端空中时间预测的平均绝对误差降低了47-49%，极端到达延误预测的平均绝对误差降低了29-57%；当用合成极值补充真实记录时，也取得了相当的提升。由于完全是数据驱动且与模型无关，TailBooster可扩展到极端事件预测至关重要且缺乏特定领域规则的领域。
英文摘要
Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operational, economic, and safety costs. Such events are rare in historical records, leaving insufficient training signal for machine learning models. Synthetic data augmentation offers a principled solution, but conventional generative models under-represent distributional tails and give no guarantee against operationally infeasible instances, such as a short air time paired with a long flight distance. No existing approach addresses both limitations for mixed-type tabular records. We propose TailBooster, a dual-layer generative framework combining generative modelling with two anomaly detection layers. A statistical layer extracts extremes via the interquartile range, supplying tail-concentrated training signal to dedicated generative models, here a Tabular Variational Autoencoder. A deep learning layer then applies autoencoder-based cleaning, discarding synthetic records that violate the operational envelope learned from historical data. The framework was evaluated on US flight records across five dimensions: diversity, statistical similarity, fidelity, operational validity, and utility, the latter two being the primary improvement targets. Data-driven cleaning markedly improved operational validity, while targeted augmentation enhanced utility for extreme-event prediction. Across six regression algorithms, training on the framework's records reduced Mean Absolute Error by 47-49% on extreme air time and 29-57% on extreme arrival delay prediction relative to conventional synthetic data, with comparable gains when real records were enriched with synthetic extremes. Being fully data-driven and model-agnostic, TailBooster extends to domains where extreme-event prediction is critical and domain-specific rules are unavailable.
发表机构
- Delft University of Technology (TU Delft)（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
