Adaptive Rotation Forests: Decision Tree Ensembles for Sequential Learning
Yu Sugawara, Satoshi Oyama, Masahito Kurihara · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
We have developed an ensemble-based approach for online machine learning: adaptive rotation forest and AD-WIN adaptive rotation forest. We focused on rotation forest, an offline supervised ensemble algorithm with a particularly high prediction accuracy while all the features are continuous. Our objective was to develop a high-performance online ensemble method that uses a process similar to that of rotation forest in an online environment. Our experiments demonstrated that the proposed approach simplifies the tree structure used for the base learners, reduces memory consumption, and improves prediction accuracy for some data streams.