New conference publication: Drift Detection over Non-stationary Data Streams using Evolving Spiking Neural Networks

Jesus L. Lobo, Javier Del Ser, Ibai Laña, Miren Nekane Bilbao, and Nikola Kasabov have published their manuscript “Drift Detection over Non-stationary Data Streams using Evolving Spiking Neural Networks” in the 12th International Symposium on Intelligent Distributed Computing (IDC) on 16th October 2018, held in Bibao (Spain).

The key topics of their research are: Online Learning, Concept Drift, and Spiking Neural Networks.

Summary: 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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