Predictive modeling for the quantity of recycled end-of-life products using optimized ensemble learners

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dc.contributor.authorXia, Hanbing
dc.contributor.authorHan, Ji
dc.contributor.authorMilisavljevic-Syed, Jelena
dc.date.accessioned2023-06-20T14:21:11Z
dc.date.available2023-06-20T14:21:11Z
dc.date.issued2023-06-09
dc.description.abstractThe rapid development of machine learning algorithms provides new solutions for predicting the quantity of recycled end-of-life products. However, the Stacking ensemble model is less widely used in the field of predicting the quantity of recycled end-of-life products. To fill this gap, we propose a Stacking ensemble model that utilizes support vector regression, multi-layer perceptrons, and extreme gradient boosting algorithms as base models, and linear regression as the meta model. The k-nearest neighbor mega-trend diffusion method is applied to avoid overfitting problems caused by a small sample data set. The grid search and time series cross validation methods are utilized to optimize the proposed model. To verify and validate the proposed model, data related to China's end-of-life vehicles industry from 2006 to 2020 is used. The experimental results demonstrate that the proposed model achieves higher prediction accuracy and generalization ability in predicting the quantity of recycled end-of-life products.en_UK
dc.identifier.citationXia H, Han J, Milisavljevic-Syed J. (2023) Predictive modeling for the quantity of recycled end-of-life products using optimized ensemble learners, Resources, Conservation and Recycling, Volume 197, October 2023, Article Number 107073en_UK
dc.identifier.eissn1879-0658
dc.identifier.issn0921-3449
dc.identifier.urihttps://doi.org/10.1016/j.resconrec.2023.107073
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19821
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSustainable reverse supply chainen_UK
dc.subjectEnd-of-life productsen_UK
dc.subjectMachine learningen_UK
dc.subjectPredictive analysisen_UK
dc.subjectEnsemble modelen_UK
dc.titlePredictive modeling for the quantity of recycled end-of-life products using optimized ensemble learnersen_UK
dc.typeArticleen_UK

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