RT Book, Section
T1 Drift detection over non-stationary data streams using evolving spiking neural networks
A1 Lobo, Jesus L.
A1 Del Ser, Javier
A1 Laña, Ibai
A1 Bilbao, Miren Nekane
A1 Kasabov, Nikola
AB 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.
PB Springer Verlag
SN 1860-949X
YR 2018
FD 2018
LK https://hdl.handle.net/11556/1589
UL https://hdl.handle.net/11556/1589
LA eng
NO 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 Studies in Computational Intelligence . Studies in Computational Intelligence , vol. 798 , Springer Verlag , pp. 82-94 . https://doi.org/10.1007/978-3-319-99626-4_8
NO Publisher Copyright: © 2018, Springer Nature Switzerland AG.
NO 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.
DS TECNALIA Publications
RD 29 jul 2024