窗口困境：为什么概念漂移检测是不恰当的
The Window Dilemma: Why Concept Drift Detection is Ill-Posed
AI总结：
本文指出概念漂移检测存在根本性问题，因漂移感知源于窗口化而非数据生成过程，且实际验证漂移事件不可行，传统批量学习方法表现更优。
摘要：
数据流的核心特征是底层数据生成过程的非平稳性，导致随时间推移分布发生变化，这种现象通常称为概念漂移。概念漂移检测器已被建立为检测此类变化（漂移）的一类方法。大多数漂移检测器通过比较数据流中的区域（窗口）并检测这些窗口是否足够不同来检测漂移。在本工作中，我们引入了窗口困境，即感知到的漂移是窗口化产生的，而不一定是底层数据生成过程。此外，我们指出漂移检测是不恰当的，主要原因是实际中验证漂移事件是不可能的。我们通过一个示例性例子，随后通过漂移检测器与各种替代适应策略的实证比较来展示这些贡献。我们的主要发现是传统批量学习技术往往比其漂移感知 counterparts 表现更好，进一步对质疑检测器在流分类中的目的提出了疑问。
英文摘要：
Non-stationarity of an underlying data generating process that leads to distributional changes over time is a key characteristic of Data Streams. This phenomenon, commonly referred to as Concept Drift, has been intensively studied, and Concept Drift Detectors have been established as a class of methods for detecting such changes (drifts). For the most part, Drift Detectors compare regions (windows) of the data stream and detect drift if those windows are sufficiently dissimilar. In this work, we introduce the Window Dilemma, an observation that perceived drift is a product of windowing and not necessarily the underlying data generating process. Additionally, we highlight that drift detection is ill-posed, primarily because verification of drift events are implausible in practice. We demonstrate these contributions first by an illustrative example, followed by empirical comparisons of drift detectors against a variety of alternative adaptation strategies. Our main finding is that traditional batch learning techniques often perform better than their drift-aware counterparts further bringing into question the purpose of detectors in Stream Classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
