Concept drift handling: A domain adaptation perspective

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.