Online Time-sharing Adaptive Ensemble Guided by Multi-type Concept Drift

Concept drift is an important feature of streaming data in the real world, and it is also an inevitable problem in data mining. In the multi-class concept drift adaptation problem, the training speed is slow, so the overall performance is good but the drift recovery speed is slow. Online Time-sharing Adaptive Ensemble Guided by Multi-type Concept Drift (OTAE), this method calculates the time offset distance between different data blocks, extracts the distance offset sequence, and identifies different drift types according to the sequence characteristics. According to different types of concept drift, combined with the regret boundary of exponential gradient descent model, the model weight is initialized asynchronously to achieve continuous asynchronous weighting of the model. Then, combined with the data classification features, the distance between inside and outside the sample class is calculated to extract the mixed density of samples, generate the density weight matrix, and realize the short-term weight control of the model. Finally, the long-term weight is combined with the short-term weight matrix to realize the two-stage weighted integration of the model. The experimental results show that the proposed method can improve the adaptability of the model to different drift types and obtain good prediction performance.