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Adaptive random forests for evolving data stream classification

Abstract
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 classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.
Type
Journal Article
Type of thesis
Series
Citation
Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfahringer, B., … Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, (Online First), 1–27. https://doi.org/10.1007/s10994-017-5642-8
Date
2017
Publisher
Springer
Degree
Supervisors
Rights
© 2017 Springer International Publishing Switzerland.This is the author's accepted version. The final publication is available at Springer via dx.doi.org/10.1007/s10994-017-5642-8