Adaptive Model Tree for Streaming Data

With an ever-growing availability of data streams the interest in and need for efficient techniques dealing with such data increases. A major challenge in this context is the accurate online prediction of continuous values in the presence of concept drift. In this paper, we introduce a new adaptive model tree (AMT), designed to incrementally learn from the data stream, adapt to the changes, and to perform real time accurate predictions at anytime. To deal with sub models lying in different subspaces, we propose a new model clustering algorithm able to identify subspace models, and use it for computing splits in the input space. Compared to state of the art, our AMT allows for oblique splits, delivering more compact and accurate models.