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Neuroevolutionary learning in nonstationary environments

Escovedo, T; Koshiyama, A; Da Cruz, AA; Vellasco, M; (2020) Neuroevolutionary learning in nonstationary environments. Applied Intelligence 10.1007/s10489-019-01591-5. (In press). Green open access

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Abstract

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.

Type: Article
Title: Neuroevolutionary learning in nonstationary environments
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10489-019-01591-5
Publisher version: https://doi.org/10.1007/s10489-019-01591-5
Language: English
Additional information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Concept drift, Adaptive learning, Nonstationary environments, Neuroevolutionary ensemble, Quantum-inspired evolution
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10092058
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