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Most existing work on classification of data streams assumes that all streaming data are labeled and the class labels are immediately available. However, in real-world applications, such as credit fraud and intrusion detection, this assumption is not always valid. Thus, it is a challenge to learn from concept drifting data streams with unlabeled data. With this motivation, we propose a Semi-supervised...
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