Scholz, Martin; Klinkenberg, Ralf - Institut für Wirtschafts- und Sozialstatistik, … - 2006
This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It...