Research on detection and integration classification based on concept drift of data stream

Abstract As a new type of data, data stream has the characteristics of massive, high-speed, orderly, and continuous and is widely distributed in sensor networks, mobile communication, financial transactions, network traffic analysis, and other fields. However, due to the inherent problem of concept drift, it poses a great challenge to data stream mining. Therefore, this paper proposes a dual detection mechanism to judge the drift of concepts, and on this basis, the integration classification of data stream is carried out. The system periodically detects data stream with the index of classification error and uses the features of the essential emerging pattern (eEP) with high discrimination to help build the integrated classifiers to solve the classification mining problems in the dynamic data stream environment. Experiments show that the proposed algorithm can obtain better classification results under the premise of effectively coping with the change of concepts