Genetic programming-based regression for temporal data

dc.contributor.authorKuranga, Cry
dc.contributor.authorPillay, Nelishia
dc.date.accessioned2021-06-11T13:20:42Z
dc.date.issued2021-09
dc.description.abstractVarious machine learning techniques exist to perform regression on temporal data with concept drift occurring. However, there are numerous nonstationary environments where these techniques may fail to either track or detect the changes. This study develops a genetic programming-based predictive model for temporal data with a numerical target that tracks changes in a dataset due to concept drift. When an environmental change is evident, the proposed algorithm reacts to the change by clustering the data and then inducing nonlinear models that describe generated clusters. Nonlinear models become terminal nodes of genetic programming model trees. Experiments were carried out using seven nonstationary datasets and the obtained results suggest that the proposed model yields high adaptation rates and accuracy to several types of concept drifts. Future work will consider strengthening the adaptation to concept drift and the fast implementation of genetic programming on GPUs to provide fast learning for high-speed temporal data.en_ZA
dc.description.departmentComputer Scienceen_ZA
dc.description.embargo2022-05-09
dc.description.librarianhj2021en_ZA
dc.description.urihttp://link.springer.com/journal/10710en_ZA
dc.identifier.citationKuranga, C., Pillay, N. Genetic programming-based regression for temporal data. Genetic Programming and Evolvable Machines 22, 297–324 (2021). https://doi.org/10.1007/s10710-021-09404-w.en_ZA
dc.identifier.issn1389-2576 (print)
dc.identifier.issn1573-7632 (online)
dc.identifier.other10.1007/s10710-021-09404-w
dc.identifier.urihttp://hdl.handle.net/2263/80294
dc.language.isoenen_ZA
dc.publisherSpringeren_ZA
dc.rights© 2021, The Author(s), under exclusive licence to Springer Science Business Media, LLC, part of Springer Nature. The original publication is available at : http://link.springer.comjournal/10710.en_ZA
dc.subjectTemporal dataen_ZA
dc.subjectConcept driften_ZA
dc.subjectModel inductionen_ZA
dc.subjectNonlinear modelen_ZA
dc.subjectPredictive modelen_ZA
dc.subjectGenetic programmingen_ZA
dc.titleGenetic programming-based regression for temporal dataen_ZA
dc.typePostprint Articleen_ZA

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Kuranga_Genetic_2021.pdf
Size:
315.8 KB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: