Ensemble learning for change-point prediction.

In this paper, we propose a novel algorithm for the problem of predicting change-points. We assume that the causes for change-points can be characterized by the time interval between a change-point and its symptom. Based on this assumption, we first generate weak classifiers for capturing each characteristic, and then build an ensemble classifier with the weak classifiers. Experimental results show our algorithm improves the F-measure by 11% in the best case.