Abstract
Drift detection in changing environments is a key factor for those active adaptive methods which require trigger mechanisms for drift adaptation. Most approaches are relied on a base learner that provides accuracies or error rates to be analyzed by an algorithm. In this work we propose the use of evolving spiking neural networks as a new form of drift detection, which resorts to the own architectural changes of this particular class of models to estimate the drift location without requiring any external base learner. By virtue of its inherent simplicity and lower computational cost, this embedded approach can be suitable for its adoption in online learning scenarios with severe resource constraints. Experiments with synthetic datasets show that the proposed technique is very competitive when compared to other drift detection techniques.
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Acknowledgements
This work was supported by the EU project Pacific Atlantic Network for Technical Higher Education and Research - PANTHER (grant number 2013-5659/004-001 EMA2), and by the Basque Government through the EMAITEK program.
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Lobo, J.L., Del Ser, J., Laña, I., Bilbao, M.N., Kasabov, N. (2018). Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_8
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