Temporal Data Mining in Dynamic Feature Spaces

  title={Temporal Data Mining in Dynamic Feature Spaces},
  author={Brent Wenerstrom and Christophe G. Giraud-Carrier},
  journal={Sixth International Conference on Data Mining (ICDM'06)},
Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done in this area. This paper presents FAE, an incremental ensemble approach to mining data subject to such concept drift. Empirical results on large data streams demonstrate promise. 
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