Adaptive random forests for data stream regression

Heitor Murilo Gomes, Jean Paul Barddal, Luis Eduardo Boiko Ferreira, Albert Bifet
2018 The European Symposium on Artificial Neural Networks  
Data stream mining is a hot topic in the machine learning community that tackles the problem of learning and updating predictive models as new data becomes available over time. Even though several new methods are proposed every year, most focus on the classification task and overlook the regression task. In this paper, we propose an adaptation to the Adaptive Random Forest so that it can handle regression tasks, namely ARF-Reg. ARF-Reg is empirically evaluated and compared to the
more » ... t data stream regression algorithms, thus highlighting its applicability in different data stream scenarios.
dblp:conf/esann/GomesBFB18 fatcat:s6qn2f5zzbddhjr4vhzynumkv4