Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

• Published in: Iraqi Journal for Computer Science and Mathematics Vol. 4; no. 2
• Main Authors: Zakariya Algamal, Firas AL-Taie, Omar Qasim
• Format: Journal Article
• Language: English
• Published: College of Education, Al-Iraqia University 20-04-2023
• Subjects: kernel methods; Pelican optimization algorithm; quasi-oppositional learning; semi-parametric model
• ISSN: 2958-0544; 2788-7421
• DOI: 10.52866/ijcsm.2023.02.02.013

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.