Inference-Time Prior Adaptation for Imbalanced Data Streams under Concept Drift
arXiv:2610.33126v1 Announce Type: new Abstract: 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, in- cluding 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 main- tains a simple sliding-window buffer and dynamically adjusts its decision rule using class pr
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