Elastic Gradient Ensemble for Concept Drift Adaptation

With the surge of streaming data, concept drift has become an important and challenging problem in streaming data mining. At present, most ensemble learning methods do not specifically identify the types of concept drift and adopt efficient ensemble adaptation strategies, resulting in uneven performance of models on different concept drift types. To address this, this paper proposes an elastic gradient ensemble for concept drift adaptation (EGE_CD). Firstly, the gradient boosting residual was extracted and the flow residual ratio was calculated to detect the drift site, and then the residual volatility was calculated to identify the type of drift. Then, the drift learners are extracted by using the change of learner loss, and the corresponding learners are deleted by combining different drift types and residual distribution characteristics to realize elastic gradient pruning. Finally, the incremental learning method was combined with the sliding sampling method to optimize the fitting process of the learner by calculating the optimal fitting rate, and then the incremental gradient growth was realized according to the change of the residual of the learner. The experimental results show that the proposed method improves the stability and adaptability of the model to different concept drift types and achieves good generalization performance.