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Online Active Learning Ensemble Framework for Drifted Data Streams
2018
IEEE Transactions on Neural Networks and Learning Systems
In practical applications, data stream classification faces significant challenges, such as high cost of labeling instances and potential concept drifting. We present a new online active learning ensemble framework for drifting data streams based on a hybrid labeling strategy that includes the following: 1) an ensemble classifier, which consists of a long-term stable classifier and multiple dynamic classifiers (a multilevel sliding window model is used to create and update the dynamic
doi:10.1109/tnnls.2018.2844332
pmid:29994730
fatcat:3bhwcx5gnbfzddmpmupnwtynmy