2022
DOI: 10.48550/arxiv.2204.06822
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Stream-based Active Learning with Verification Latency in Non-stationary Environments

Andrea Castellani,
Sebastian Schmitt,
Barbara Hammer

Abstract: Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over time (concept drift), the model needs to continuously adapt to new data statistics. Stream-based Active Learning (AL) approaches address this problem by interactively querying a human expert to provide new data labels for the most recent samples, within a limited budget. Existing AL strategies assume that labels are immediately availabl… Show more

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