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Adaptive random forests for evolving data stream classification
2017
Machine Learning
Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for
doi:10.1007/s10994-017-5642-8
fatcat:jn7gytjujfeuhjilhtpop6gsci