Accelerating the convergence of concept drift based on knowledge transfer

Abstract

Concept drift detection and processing is an important issue in streaming data mining. When concept drift occurs, online learning model often cannot quickly adapt to the new data distribution due to the insufficient newly distributed data, which may lead to poor model performance. Currently, most online learning methods adapt to new data distributions after concept drift through autonomous adjustment of the model, but they may often fail to update the model to a stable state quickly. To solve these problems, this paper proposes an accelerating convergence method of concept drift based on knowledge transfer ($\mathrm{ACC}\text{\_}\mathrm{KT}$). It extracts the most valuable information from the source domain (pre-drift data), and transfers it to the target domain (post-drift data), to realize the update of the ensemble model by knowledge transfer. Besides, different knowledge transfer patterns are adopted to accelerate convergence of model performance when different types concept drift occur. Experimental results show that the proposed method has an obvious acceleration effect on the online learning model after concept drift.