提出双通道架构，同时高效检测数据流中的异常点与概念漂移
Robust Outlier Detection and Low-Latency Concept Drift Adaptation for Data Stream Regression: A Dual-Channel Architecture
- 设计双通道机制，分别处理异常点与概念漂移的快速响应与深度分析
- 引入EWMAD-DT检测器，能动态区分突变与渐变漂移，提升检测精度
- 适用于实时数据流场景，尤其适合异常与漂移共存的高要求应用
异常检测与概念漂移检测是数据分析中的两大挑战。现有研究多将二者分开处理，但在回归任务中联合检测机制仍不充分，因输出空间连续，漂移与异常难以区分。为此，本文提出一种新型鲁棒回归框架，实现异常与概念漂移的联合检测。具体地，设计双通道决策流程，将预测残差分为快速响应通道（用于过滤点异常）与深度分析通道（用于诊断漂移）。进一步提出指数加权移动绝对偏差类型区分（EWMAD-DT）检测器，通过动态阈值自动区分突变与增量漂移。在合成及真实数据集上的大量实验表明，该统一框架结合EWMAD-DT，在异常点与概念漂移共存时仍具备优异检测性能。
原文摘要 · Abstract (English)
Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the continuous nature of output spaces makes distinguishing drifts from outliers inherently challenging. To address this, we propose a novel robust regression framework for joint outlier and concept drift detection. Specifically, we introduce a dual-channel decision process that orchestrates prediction residuals into two coupled logic flows: a rapid response channel for filtering point outliers and a deep analysis channel for diagnosing drifts. We further develop the Exponentially Weighted Moving Absolute Deviation with Distinguishable Types (EWMAD-DT) detector to autonomously differentiate between abrupt and incremental drifts via dynamic thresholding. Comprehensive experiments on both synthetic and real-world datasets demonstrate that our unified framework, enhanced by EWMAD-DT, exhibits superior detection performance even when point outliers and concept drifts coexist.
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