Open AccessSmoothing parameter selection in kernel nonparametric regression using bat optimization algorithm
Author(s) -
Marwah Yahya Mustafa,
Zakariya Yahya Algamal
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1897/1/012010
Subject(s) - smoothing , nonparametric regression , nonparametric statistics , kernel (algebra) , kernel smoother , kernel regression , context (archaeology) , selection (genetic algorithm) , computer science , mathematical optimization , kernel method , mathematics , algorithm , artificial intelligence , statistics , support vector machine , radial basis function kernel , paleontology , combinatorics , biology
In the context of kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this paper, a bat optimization algorithm method is proposed to choose the smoothing parameter in Nadaraya-Watson kernel nonparametric regression. The proposed method will efficiently help to find the best smoothing parameter with a high prediction. The proposed method is compared with four famous ` of prediction capability.