Concept drift occurs when a target concept changes over time. We present a new method for learning shifting target concepts during concept drift. The method, called Concept Drift Committee (CDC), uses a weighted committee of hypotheses that votes on the current classification. When a committee member's voting record drops below a minimum threshold, the member is forced to retire. A new committee member then takes the open place on the committee. The algorithm is compared to a leading algorithm on several benchmarks. The results indicate that using a committee to track drift has several advantages over traditional window-based approaches.

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Technical Report AI03-302, Department of Computer Sciences, The University of Texas at Austin, 2003.

@techreport{stanley:utcstr03-302, title={Learning Concept Drift with a Committee of Decision Trees}, author={Kenneth O. Stanley}, number={AI03-302}, institution={Department of Computer Sciences, The University of Texas at Austin}, url="http://nn.cs.utexas.edu/?stanley:utcstr03-302", year={2003} }