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Many applications track streaming data for actionable alerts, which may include, for example, network intrusions, transaction frauds, biosurveilence abnormalities, etc. Some stream classification models are built for this purpose. Due to concept drifts, maintaining a model's up-to-dateness has become one of the most challenging tasks in mining data streams. State of the art approaches, including both the incrementally updated classifiers and the ensemble classifiers, have proved that model update is a very costly process. In this paper, we show that reducing model granularity reduces update cost, as models of fine granularity enable us to efficiently pinpoint local components in the model that are affected by the concept drift. It also enables us to derive new model components to reflect the current data distribution, thus avoiding expensive updates on a global scale. Furthermore, those actionable alerts being monitored are usually rare occurring. The existing stream classifiers cannot handle this problem. We address this problem and show that the low granularity classifier handles rare events on stream data with ease. Experiments on real and synthetic data show that our approach is able to maintain good prediction accuracy at a fraction of model updating cost of state of the art approaches.
Classification, data stream, concept drift, association rule
Xiaochen Wu, Baile Shi, Wei Wang, Haixun Wang, Peng Wang, "A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution", IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 1202-1213, September 2007, doi:10.1109/TKDE.2007.1057
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