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A Bayesian Approach to Concept Drift
Stephen H Bach · Mark Maloof

Mon Dec 06 12:00 AM -- 12:00 AM (PST) @

To cope with concept drift, we placed a probability distribution over the location of the most-recent drift point. We used Bayesian model comparison to update this distribution from the predictions of models trained on blocks of consecutive observations and pruned potential drift points with low probability. We compare our approach to a non-probabilistic method for drift and a probabilistic method for change-point detection. In our experiments, our approach generally yielded improved accuracy and/or speed over these other methods.

Author Information

Stephen H Bach (Brown University)
Mark Maloof (Georgetown University)

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