Lobo, Jesus L.Del Ser, JavierLaña, IbaiBilbao, Miren NekaneKasabov, Nikola2024-07-242024-07-242018Lobo , 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_81860-949Xhttps://hdl.handle.net/11556/1589Publisher Copyright: © 2018, Springer Nature Switzerland AG.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.13enginfo:eu-repo/semantics/restrictedAccessbook part10.1007/978-3-319-99626-4_8Concept driftOnline learningSpiking neural networksArtificial Intelligencehttp://www.scopus.com/inward/record.url?scp=85053438548&partnerID=8YFLogxK