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提出 PAI-KNN，一种通过推理时先验调整而非内存修改来适应概念漂移与类不平衡的 KNN 数据流分类器，在合成数据流基准上取得了与最先进方法竞争的性能。
This paper proposes a causal framework for understanding concept drift in data streams using Structural Causal Models, with a taxonomy and generator for simulating and evaluating drift events in non-stationary environments.
RankShift is a novel in-database method for detecting and explaining categorical shifts in data streams, using Pearson scores to identify responsible categories, and it shows competitive performance against autoencoders on log datasets.