Detecting and Adapting to Concept Drift in Continually Evolving Stochastic Processes
Sunanda Gamage, Upeka Kanchana Premaratne · 2017
Many real world stochastic processes are non-stationary, which means that the probability distribution that generates data samples is time-varying. In the context of machine learning, this phenomenon is known as concept drift. It is important that machine learning models are able to adapt to concept drift in order to prevent degradation in accuracy. In this paper, we present two algorithms for drift detection and adaptation.