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Nowadays with a growing number of online controlling systems in theorganization and also a high demand of monitoring and stats facilities thatuses data streams to log and control their subsystems, data stream miningbecomes more and more vital. Hoeffding Trees (also called Very Fast DecisionTrees a.k.a. VFDT) as a Big Data approach in dealing with the data stream forclassification and regression problems showed good performance in handlingfacing challenges and making the possibility of any-time prediction. Althoughthese methods outperform other methods e.g. Artificial Neural Networks (ANN)and Support Vector Regression (SVR), they suffer from high latency in adaptingwith new concepts when the statistical distribution of incoming data changes.In this article, we introduced a new algorithm that can detect and handleconcept drift phenomenon properly. This algorithms also benefits from faststartup ability which helps systems to be able to predict faster than otheralgorithms at the beginning of data stream arrival. We also have shown that ourapproach will overperform other controversial approaches for classification andregression tasks.

Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm