Study on a Classification Model of Data Stream based on Concept Drift

Abstract

In the data stream classification process, in addition to the solution of massive and realtime data stream, the dynamic changes of the need to focus and study. From the angle of detecting concept drift, according to the dynamic characteristics of the data stream. This paper proposes a new classification method for data stream based on the combined use of concept drift detection and classification model. The data stream classification model can’t adapt to concept drift problem to solve. Before the model classification, the use of information entropy to judge the data block concept drift, the concept of history to have appeared, the use of a classifier pool mechanism to save it, to makes the classification model has stronger resistance to concept drift.