用瓦瑟斯坦距离改进集成检测器聚合，提升高维数据突变点识别精度。
WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
- 基于瓦瑟斯坦距离设计新型集成聚合方法WWAggr
- 在多个数据集上显著优于平均法等传统聚合方式
- 无需人工设定阈值，适合实际部署的异常检测场景
突变点检测（CPD）旨在识别数据流中分布突变的时刻。现实世界中的高维CPD因数据模式复杂且违背常见假设而极具挑战性。当前基于单一深度神经网络的先进检测器尚未达到理想性能。与此同时，集成方法能提供更鲁棒的解决方案并提升效果。本文研究深度突变点检测器的集成，并发现标准预测聚合技术（如平均）次优，未能考虑问题特性。为此，我们提出WWAggr——一种基于瓦瑟斯坦距离的新型任务特定集成聚合方法。该方法具有通用性，适用于多种深度CPD模型集成。此外，与现有方案不同，我们实质性解决了长期存在的决策阈值选择难题。
原文摘要 · Abstract (English)
Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-the-art detectors have yet to achieve perfect quality. Concurrently, ensembling provides more robust solutions, boosting the performance. In this paper, we investigate ensembles of deep change point detectors and realize that standard prediction aggregation techniques, e.g., averaging, are suboptimal and fail to account for problem peculiarities. Alternatively, we introduce WWAggr -- a novel task-specific method of ensemble aggregation based on the Wasserstein distance. Our procedure is versatile, working effectively with various ensembles of deep CPD models. Moreover, unlike existing solutions, we practically lift a long-standing problem of the decision threshold selection for CPD.
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