On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams

Using classification models, applications track streaming data to detect actionable alerts, which may include, for example, network intrusions, transaction frauds, biosurveilence abnormality, etc. Due to concept drifts, maintaining a model’s uptodateness 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 introduce the concept of model granularity, and show that reducing model granularity will reduce update cost. Indeed, 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 components that can easily integrate with the model to reflect the current data distribution, thus avoiding avoid expensive updates on a global scale. 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.

By: Peng Wang; Haixun Wang; Xiaochen Wu; Wei Wang; Baile Shi

Published in: RC23711 in 2005


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