Data stream classification under concept drift and class imbalance requires models to adapt continuously while maintaining strong minority-class performance. Many existing approaches rely on active memory management, including instance removal, memory restructuring, resampling, and synthetic data generation.
We present PAI-KNN, a k-nearest neighbor classifier that adapts via inference-time prior adjustment rather than memory modification. PAI-KNN maintains a simple sliding-window buffer and dynamically adjusts its decision rule using class prior estimates derived from recent observations.
Evaluated on a multitude of synthetic data streams covering various imbalance and drift scenarios, PAI-KNN achieves competitive performance with state-of-the-art methods, performing particularly well under scenarios involving dynamic class imbalance and concept drift.
These findings highlight the potential of inference-time adaptation in challenging non-stationary environments and suggest that adaptation in memory-based stream classifiers need not rely exclusively on memory modification.