Abstract:Concept drift is a big obstacle in the field of mining stream data. By dynamic modifying the ensemble classifier, SEA can effectively catch concept drift for mining stream data. The method of SEA modifying the ensemble classifier is direct dropping a base classifier of the lowest weight. That means the algorithm abandon a learned concept, but the algorithm will waste time to learn the abandoned concept, as a result this leads to a low-level effective algorithm. In this paper, a new algorithm ECRRC(Ensemble Classifiers Retrieving Repeated Concept ) with the ability of retrieving the old concept is proposed to reuse the old classifier. Facing the concept repeating, ECRRC need not learn again for mining stream data. Besides the method of storing and retrieving the concept is presented in this paper. The experimental results show that the algorithm in this paper raises classifying data stream efficiency.