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Regular data classification techniques are based mainly on two strong assumptions: (1) the existence of a reasonably large labeled set of data to be used in training; and (2) future input data instances conform to the distribution of the training set, i.e. data distribution is stationary along time. However, in the case of data stream classification, both of the aforementioned assumptions are difficult...
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