Determining the Training Window for Small Sample Size Classification with Concept Drift

  • Authors:

  • Indre Žliobaite;Ludmila I. Kuncheva

  • Affiliations:

  • -;-

  • Venue:
  • ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
  • Year:
  • 2009

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Abstract

We consider classification of sequential data in the presence of frequent and abrupt concept changes. The current practice is to use the data after the change to train a new classifier. However, if the window with the new data is too small, the classifier will be undertrained and hence less accurate that the "old'' classifier. Here we propose a method (called WR*) for resizing the training window after detecting a concept change. Experiments with synthetic and real data demonstrate the advantages of WR* over other window resizing methods.