Learning Concept Drift with a Committee of Decision Trees (2003)
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.
Technical Report AI03-302, Department of Computer Sciences, The University of Texas at Austin.

Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu