Incremental learning in nonstationary environments with controlled forgetting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
Data stream classification with artificial endocrine system
Applied Intelligence
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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.