  Nowadays, every device connected to the Internet generates an ever-growing
stream of data (formally, unbounded). Machine Learning on unbounded data
streams is a grand challenge due to its resource constraints. In fact, standard
machine learning techniques are not able to deal with data whose statistics is
subject to gradual or sudden changes without any warning. Massive Online
Analysis (MOA) is the collective name, as well as a software library, for new
learners that are able to manage data streams. In this paper, we present a
research study on streaming rebalancing. Indeed, data streams can be imbalanced
as static data, but there is not a method to rebalance them incrementally, one
element at a time. For this reason we propose a new streaming approach able to
rebalance data streams online. Our new methodology is evaluated against some
synthetically generated datasets using prequential evaluation in order to
demonstrate that it outperforms the existing approaches.
