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Machine learning models deployed in real world applications, operate in a dynamic environment where the datadistribution can change constantly. These changes, calledconcept drifts, cause the performance of the learned modelto degrade over time. As such it is essential to detect andadapt to changes in the data, for the model to be of any realuse. While, model adaptation requires labeled data (for retraining),...
The data streams in many applications are characterized by imbalanced class distribution. The pattern in data streams may also change over time and therefore, the classification model should be adjusted to maintain performance. Hence, a new set of labeled samples should be provided which is not an easy task, since labeling is expensive and time consuming. In this paper, we propose Reduced Labeled...
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