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Learning to Classify Data Streams with Imbalanced Class Distributions

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 propose a method called Boundary Definition, which relies on building the classifiers by stressing on the boundary cases as the streams arrive. We employ a sequential validation framework, which we believe is the most meaningful option in the context of streaming imbalanced data.