Countering the concept-drift problems in big data by an incrementally optimized stream mining model
The paper investigates the performance of incremental decision trees for concept drift, including VFDT, ADWIN, and iOVFDT. The computer simulation results show that iOVFDT has higher accuracy and less memory consumption than the original VFDT. The proposed method is useful and significant in fields of big data mining especially when concept-drift problem arises.
concept drift data stream mining very fast decision tree