Concept Drift Convergence Method Based on Adaptive Deep Ensemble Networks

Concept drift is an important and challenging problem in streaming data mining field. However, most existing methods can only deal with linear or simple nonlinear mappings. In spite of the ability of fitting nonlinear functions, neural network models have difficulty in adjusting dynamically according to the changing data streaming because only one sample or one batch of samples is available at a time for model training in the context of streaming data mining task. In order to solve above problem, the thought of gradient boosting algorithm is introduced to solve the problem of streaming data mining task with concept drift and a concept drift convergence method based on adaptive deep ensemble networks (CD_ADEN) is proposed. The proposed model combines several shallow neural networks as base leaner, and subsequent base learner corrects the output of precedent base learner to make the final output achieve high real-time generalization performance. Besides, because of the high convergence speed of shallow neural network, the proposed model will quickly recover from accuracy decrease caused by concept drift. The experimental results on multiple datasets show that the average real-time accuracy of the proposed CD_ADEN method is significantly improved compared with the comparative methods, the average real-time accuracy is improved by 1%−5%, and the average ordinal value ranks first in the comparison of several algorithms. It shows that the proposed model can correct the error of the pre-order output, and the learning model can quickly recover from the accuracy drop caused by concept drift, which improves the real-time generalization performance of the online learning model.