RESUMO
Data production has followed an increased growth in the last years, to the point that traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of generated data. Stream or online ML presents itself as a viable solution to deal with the dynamic nature of streaming data. Besides coping with the inherent challenges of streaming data, online ML solutions must be accurate, fast, and bear a reduced memory footprint. We propose a new decision tree-based ensemble algorithm for online ML regression named online extra trees (OXT). Our proposal takes inspiration from the batch learning extra trees (XT) algorithm, a popular and faster alternative to random forest (RF). While speed and memory costs might not be a central concern in most batch applications, they become crucial in data stream data learning. Our proposal combines subbagging (sampling without replacement), random tree split points, and model trees to deliver competitive prediction errors and reduced computational costs. Throughout an extensive experimental evaluation comprising 22 real-world and synthetic datasets, we compare OXT against the state-of-the-art adaptive RF (ARF) and other incremental regressors. OXT is generally more accurate than its competitors while running significantly faster than ARF and expending significantly less memory.