Abstract:When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data si… Show more
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