School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China; Key Lab of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, Shanxi, China.
School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China.
Neural Netw. 2021 Oct;142:437-456. doi: 10.1016/j.neunet.2021.06.027. Epub 2021 Jul 2.
Concept drift is an important issue in the field of streaming data mining. However, how to maintain real-time model convergence in a dynamic environment is an important and difficult problem. In addition, the current methods have limited ability to deal with the problem of streaming data classification for complex nonlinear problems. To solve these problems, a selective ensemble-based online adaptive deep neural network (SEOA) is proposed to address concept drift. First, the adaptive depth unit is constructed by combining shallow features with deep features and adaptively controls the information flow in the neural network according to changes in streaming data at adjacent moments, which improves the convergence of the online deep learning model. Then, the adaptive depth units of different layers are regarded as base classifiers for ensemble and weighted dynamically according to the loss of each classifier. In addition, a dynamic selection of base classifiers is adopted according to the fluctuation of the streaming data to achieve a balance between stability and adaptability. The experimental results show that the SEOA can effectively contend with different types of concept drift and has good robustness and generalization.
概念漂移是流数据挖掘领域的一个重要问题。然而，如何在动态环境中保持实时模型收敛是一个重要且困难的问题。此外，当前的方法对于处理复杂非线性问题的流数据分类问题的能力有限。为了解决这些问题，提出了一种基于选择性集成的在线自适应深度神经网络（SEOA）来解决概念漂移问题。首先，通过将浅层特征与深层特征相结合，构建自适应深度单元，并根据相邻时刻流数据的变化自适应地控制神经网络中的信息流，从而提高在线深度学习模型的收敛性。然后，将不同层的自适应深度单元视为基分类器进行集成，并根据每个分类器的损失动态加权。此外，根据流数据的波动采用动态选择基分类器，以实现稳定性和适应性之间的平衡。实验结果表明，SEOA 可以有效地应对不同类型的概念漂移，具有良好的鲁棒性和泛化性。
