Microcluster-based incremental ensemble learning for noisy, nonstationary data streams

Sanmin Liu, Shan Xue*, Fanzhen Liu, Jieren Cheng, Xiulai Li, Chao Kong, Jia Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines.

Original languageEnglish
Article number6147378
Number of pages12
JournalComplexity
Volume2020
DOIs
Publication statusPublished - 2020

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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