document: Suppressing model overfitting in mining concept-drifting data streams Mining data streams of changing class distributions is important for real-time business decision support . The stream classifier must evolve to reflect the current class distribution . This poses a serious challenge . On the one hand , relying on historical data may increase the chances of learning obsolete models . On the other hand , learning only from the latest data may lead to biased classifiers , as the latest data is often an unrepresentative sample of the current class distribution . The problem is particularly acute in classifying rare events , when , for example , instances of the rare class do not even show up in the most recent training data . In this paper , we use a stochastic model to describe the concept shifting patterns and formulate this problem as an optimization one : from the historical and the current training data that we have observed , find the most-likely current distribution , and learn a classifier based on the most-likely distribution . We derive an analytic solution and approximate this solution with an efficient algorithm , which calibrates the influence of historical data carefully to create an accurate classifier . We evaluate our algorithm with both synthetic and real-world datasets . Our results show that our algorithm produces accurate and efficient classification .
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