提出可模拟真实场景的数据流评估框架，解决标签延迟下的模型评价难题。
Structuring the Processing Frameworks for Data Stream Evaluation and Application
- 构建结构化框架，模拟真实中标签延迟与有限访问的约束。
- 揭示漂移检测与分类方法间的关联，提升评估可靠性。
- 适合研究数据流学习、在线模型评估的科研人员使用。
本文针对数据流处理框架在真实应用环境中的评估问题展开研究。现有实验常假设可无限制、即时获取标签，导致对模型性能的评估失真。为此，通过梳理现有数据流处理方法并验证其在模拟环境中的表现，提出了一个数据流处理框架的分类体系。该体系明确了漂移检测与分类方法之间的关联，并考虑了标签延迟这一自然现象的影响，从而为数据流分类方法的可靠评估提供支持。
原文摘要 · Abstract (English)
The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliably evaluate the data stream classification methods, considering the constraints of delayed and limited label access. The current experimental evaluation often boundlessly exploits the assumption of their complete and immediate access to monitor the recognition quality and to adapt the methods to the changing concepts. The problem is leveraged by reviewing currently described methods and techniques for data stream processing and verifying their outcomes in simulated environment. The effect of the work is a proposed taxonomy of data stream processing frameworks, showing the linkage between drift detection and classification methods considering a natural phenomenon of label delay.
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