Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm
This work addresses the need for real-time adaptation in online controlling systems and monitoring facilities, though it appears incremental as it builds upon existing Hoeffding Tree methods.
The authors tackled the problem of concept drift in data stream mining by introducing Hybrid Forest, an algorithm that detects and handles changes in data distribution, resulting in faster startup and outperforming existing methods like Hoeffding Trees, ANNs, and SVRs in classification and regression tasks.
Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.