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In data stream, concept drift often occurs in an unpredictable way, the classifier model which was learned from previous data is not accurate to the current data, so regular updating of the model is necessary. Moreover, updating too frequently will cause a negative impact on the clustering accuracy and the analysis of subsequent data. This paper proposed a model-updating mechanism for data streams with concept drift, and the updating mechanism is based on the calculation of correlation coefficient and information entropy. In terms of cumulative sum of entropy values and the predefined threshold, the system determined whether to update the model. After the model updating, classifier pool saves the existing model, and each classifier is used to describe a previous concept so that the system can detect recurring concepts drift and reduce the updating frequency.

Model updating mechanism of concept drift detection in data stream based on classifier pool