Abstract
In a time in which streaming data becomes the new normal in Machine Learning problems, to the detriment of batch data, new challenges arise. In the past, a data source would be static in the sense that all data were known at the moment of the training of the model. A model would be trained and it would be in use for relatively long periods of time. Nowadays, data arrive in real-time and their statistical properties may also change over time, rendering trained models outdated. In this paper we propose an approach to deal with the concept drift problem with minimal computational effort. Specifically, we continuously update an ensemble with new weak learners and adjust their weights according to their performance. This approach is suitable to be used in real-time in the form of an ever-evolving model that adapts to change in the data.
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Acknowledgements
This work was supported by the Northern Regional Operational Program, Portugal 2020 and European Union, trough European Regional Development Fund (ERDF) in the scope of project number 39900—31/SI/2017, and by FCT—Fundação para a Ciência e Tecnologia within projects UIDB/04728/2020 and UID/CEC/00319/2019.
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Ramos, D., Carneiro, D., Novais, P. (2022). Using Evolving Ensembles to Deal with Concept Drift in Streaming Scenarios. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_6
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DOI: https://doi.org/10.1007/978-3-030-96627-0_6
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