Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm
Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. However, it is not easy to determine the optimal model beforehand. Therefore, in this paper, we look at how to ch...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
College of Education, Al-Iraqia University
2023-04-01
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Series: | Iraqi Journal for Computer Science and Mathematics |
Subjects: | |
Online Access: | https://journal.esj.edu.iq/index.php/IJCM/article/view/404 |
Summary: | Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. However, it is not easy to determine the optimal model beforehand. Therefore, in this paper, we look at how to choose a hyper-parameter in a kernel semi-parametric regression model. A quasi-oppositional learning pelican optimization algorithm strategy is used to select the smoothness parameter. In comparison to other competitor approaches, simulation results revealed that the suggested method, the quasi-oppositional learning pelican optimization algorithm, is superior in terms of MSE. The experimental findings and statistical analysis show that when compared to the CV and GCV, our proposed quasi-oppositional learning pelican optimization algorithm provides greater performance in terms of computational time.
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ISSN: | 2958-0544 2788-7421 |