Genetic programming-based regression for temporal data

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@Article{Kuranga:GPEM,

• author = "Cry Kuranga and Nelishia Pillay",
• title = "Genetic programming-based regression for temporal data",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2021",
• volume = "22",
• number = "3",
• pages = "297--324",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Temporal data, Concept drift, Model induction, Nonlinear model, Predictive model",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-021-09404-w",
• abstract = "Various 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 non-stationary 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.",
• notes = "Department of Computer Science, University of Pretoria, Lynnwood Road, Hillcrest, Pretoria, 0002, South Africa",

}

Genetic Programming entries for Cry Kuranga Nelishia Pillay

Citations