Vertical and Horizontal Partitioning in Data Stream Regression Ensembles
Jean Paul Barddal · 2019
Data stream mining is an emerging topic in machine learning that targets the creation and update of predictive models over time as new data becomes available. Regarding existing works, classification is the most widely tackled task, which leaves regression nearly untouched. In this paper, the focus relies on ensemble learning for data stream regression, more specifically on vertical and horizontal data partitioning techniques. The goal is to determine whether and under which conditions partitioning can lessen the error rates of different types of learners in the data stream regression task. The proposed method combines vertical and horizontal partitioning, and it is compared with and against different types of learners and existing ensembles.