Classifying data streams by stacking ensemble
基于堆叠集成的数据流分类*

Ensemble learning is a general method for classifying data streams. In order to get a better classification, this paper proposed a general framework for classifying data streams by stacking ensemble. Built another classifier to combine base classifiers. Experiments show that comparing majority vote or weight vote ensemble classifiers, stacking ensemble classifiers has stronger ability in adapting to concept drifting and higher accuracy.