Concept drift detection and accelerated convergence of online learning

Streaming data has become an important form in the era of big data, and the concept drift, as one of the most important problem of it, is often studied deeply. However, similar to true concept drift, noise and too small training samples will also lead to the classification performance fluctuation, which is easy to confuse with true concept drift. To solve this problem, an improved concept drift detection method is proposed, and the accelerated convergence of the model after concept drift is also studied. Firstly, the effective fluctuation sites can be obtained by group detection method. Secondly, the authenticity of concept drift can be determined by tracking the testing accuracy of reference sites near the effective fluctuation site. Lastly, in the convergence acceleration stage, the time sequential distance is designed to measure the similarity of these sequential data blocks during different time periods, and the noncritical disturbance data with the largest time sequential distance are removed sequentially to improve the convergence speed of the model after concept drift occurs. The experimental results demonstrate that the proposed method not only produces better identification results in distinguishing true and false concept drift but also improves the convergence speed of the model.