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.