Learning to Classify Data Streams with Imbalanced Class Distributions

  title={Learning to Classify Data Streams with Imbalanced Class Distributions},
  author={Ryan Lichtenwalter and Nitesh V. Chawla},
Streaming data is pervasive in a multitude of data mining applications. One fundamental problem in the task of mining streaming data is distributional drift over time. Streams may also exhibit high and varying degrees of class imbalance, which can further complicate the task. In scenarios like these, class imbalance is particularly difficult to overcome and has not been as thoroughly studied. In this paper, we consider the issue of high class imbalacne in conjunction with data streams. We… CONTINUE READING

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