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Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift (2003)
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BibTeX
@INPROCEEDINGS{Kolter03dynamicweighted,
author = {Jeremy Z. Kolter and Marcus A. Maloof},
title = {Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift},
booktitle = {},
year = {2003},
pages = {123--130}
}
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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.
