Learning Concept Drift with a Committee of Decision Trees
نویسنده
چکیده
Concept drift occurs when a target concept changes over time. I 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 minimal 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 a number of concept drift problems. The results show that using a committee to track drift has several advantages over more customary window-based approaches.
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