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In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but cannot handle well rather skewed (e.g., few positives but lots of negatives) and stochastic distributions, which are typical in many data stream applications. In this paper, we propose a new approach to mine data streams by estimating reliable posterior probabilities using an ensemble of models to match thedoi:10.1137/1.9781611972771.1 dblp:conf/sdm/GaoFHY07 fatcat:6ips4ugs2nfwjncopqk27tgn34