|
Published Articles >> Table of Contents >> Abstract
Third IEEE International Conference on Data Mining (ICDM'03)
p. 123
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
Jeremy Z. Kolter, Georgetown University, Washington, DC
Marcus A. Maloof, Georgetown University, Washington, DC
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250911
Send link to a friend
| Abstract |
|
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any on-line learner for concept drift. Dynamic Weighted Majority (DWM) maintains an ensemble of base learners, predicts using a weighted-majority vote of these "experts", and dynamically creates and deletes experts in response to changes in performance. We empirically evaluated two experimental systems based on the method using incremental naive Bayes and Incremental Tree Inducer (ITI) as experts. For the sake of comparison, we also included Blum's implementation of Weighted Majority. On the STAGGER Concepts and on the SEA Concepts, results suggest that the ensemble method learns drifting concepts almost as well as the base algorithms learn each concept individually. Indeed, we report the best overall results for these problems to date.
|
 Additional Information
|
Citation:
Jeremy Z. Kolter, Marcus A. Maloof,
"Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift,"
icdm,
p. 123,
Third IEEE International Conference on Data Mining (ICDM'03),
2003
|
|
