A Kind of Improved SPRINT Classification Algorithm Adaptive to the Change of Concept Drift

With the wide application of data streams mining, the study on data streams classification algorithm with concept drift has become an important piece of work. In light of the characteristics of data streams, this paper puts forward a kind of improved SPRINT classification algorithm adaptive to the occurrence of concept drift. It is proved by experiments that it can automatically adjust the number of training window and new sample during model reconstruction according to the current situation of concept drift and consume less sources and have higher classification accuracy.