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In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction model from noisy stream data to accurately predict future samples. For noisy data sources, most existing works rely on data preprocessing techniques to cleanse noisy samples before...
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