Inference-Time Prior Adaptation for Imbalanced Data Streams under Concept Drift

The paper introduces PAI-KNN, a k‑nearest neighbor classifier designed for data stream classification with concept drift and class imbalance. Unlike traditional methods that modify memory through instance removal or resampling, PAI‑KNN keeps a simple sliding‑window buffer and adapts its decision rule at inference time by adjusting class priors based on recent observations. Experiments on synthetic streams with varying imbalance and drift show that PAI‑KNN performs competitively with state‑of‑the‑art techniques, especially when dynamic class imbalance and concept drift are present.

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