DME: An adaptive and Just-in-time weighted ensemble learning method for classifying block-based concept drift steam

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Feng, Baoquan, Gu, Yan, Yu, Hualong, Yang, Xibei, Gao, Shang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Buffers; Classification algorithms; Classifiers; concept drift; Data stream; Data transmission; Drift; Ensemble learning; Gaussian mixture model; Heuristic algorithms; Just in time; Kullback-Leibler Divergence; Machine learning; Prediction algorithms; Probabilistic models; Training data; weighted ensemble learning

This study proposes a novel incremental learning algorithm called distribution matching ensemble (DME) in context of adaptive weighted ensemble learning. In particular, DME estimates the distribution of each received data block by Gaussian mixture model (GMM) and reserves the corresponding distribut...