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Concept Drift Handling: A Domain Adaptation Perspective

EXPERT SYSTEMS WITH APPLICATIONS(2023)

Sharif Univ Technol

Cited 7|Views10
Abstract
Data stream prediction is challenging when concepts drift, processing time, and memory constraints come into account. Concept drift refers to changes in data distribution over time that reduces prediction systems’ accuracy. We present a method for handling concept drift with a domain adaptation approach (CDDA) in a data stream. The proposed method passively deals with the concept drift by using the domain adaptation approaches with multiple sources while reducing the model execution time and memory consumption. We introduce two variants of CDDA to transfer the information in the multi-source windows to the target window: weighted multi-source CDDA and multi-source feature alignment CDDA. Then, we theoretically study the behavior of CDDA and find the generalization bound of CDDA for the data stream prediction problem. Moreover, an extensive set of experiments conducted on both synthetic and real-world data streams confirms the validity and excellent performance of the proposed approach. Our code is available at https://github.com/mahan66/cdda.
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Key words
Data stream prediction,Domain adaptation,Concept drift,Generalization bound,Uniform entropy number
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要点】:本文提出了一种基于领域适应方法(CDDA)处理数据流中的概念漂移问题,通过利用多源数据降低模型执行时间和内存消耗,同时引入两种CDDA变体以优化信息传递。

方法】:通过使用多源领域适应方法,将多源窗口中的信息传递到目标窗口,从而适应数据流中的概念漂移。

实验】:在合成和真实世界的数据流上进行了大量实验,验证了所提出方法的有效性和卓越性能,具体数据集名称未在摘要中提及。