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Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task...
Recent years have witnessed an increasing number of studies in stream mining, which aim at building an accurate model for continuously arriving data. Somehow most existing work makes the implicit assumption that the training data and the yet-to-come testing data are always sampled from the "same distribution", and yet this "same distribution" evolves over time. We demonstrate that...
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