Learning When to Adapt to Concept Drift from a Single Predictive Trajectory

Concept drift detectors typically trigger adaptation when the detector statistic crosses a predefined threshold. While the threshold controls detector sensitivity, the ultimate goal of data stream learning is to maintain predictive performance under drift. Accordingly, a threshold adjustment method has been proposed to adjust the detector threshold according to the performance of the predictor. However, the existing method determines whether to adapt and adjusts the detector threshold by comparing the predictive performance of different thresholding schemes using future observations. This can be impractical in many real-world streaming settings, where threshold adjustment and adaptation decisions must be made online as data arrive. To address this limitation, we propose a single-trajectory framework that jointly adjusts the detector threshold and determines whether to adapt using information observed along the realized predictive trajectory. This allows the threshold to be adjusted and adaptation decisions to be made online without waiting for future observations. We provide an optimization interpretation of our threshold adjustment rule, analyze its stability, and derive a dynamic regret bound. Extensive experiments across multiple classifiers, drift detectors, datasets, and adaptation settings show that the proposed framework achieves predictive performance comparable to the state of the art methods while reducing computation overhead.