Concept Drift Detection Through Resampling
Maayan Harel, Shie Mannor, Ran El‐Yaniv, Koby Crammer · 2014
Detecting changes in data-streams is an impor-tant part of enhancing learning quality in dy-namic environments. We devise a procedure for detecting concept drifts in data-streams that re-lies on analyzing the empirical loss of learning algorithms. Our method is based on obtaining statistics from the loss distribution by reusing the data multiple times via resampling. We present theoretical guarantees for the proposed proce-dure based on the stability of the underlying learning algorithms. Experimental results show that the method has high recall and precision, and performs well in the presence of noise. 1.