2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 11 , Issue 1 , PP: 57-69, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Firas A. Yonis AL-Taie 1 * , Zakariya Yahya Algamal 2 , Omar Saber Qasim 3
Doi: https://doi.org/10.54216/FPA.110104
This paper investigates the process of selecting a hyperparameter for use in a kernel semiparametric regression model for fusion data, which is an important tool in various scientific study fields. The selection of the best model to use in advance is not a simple task, and one of the most fascinating current advances in the application is the use of hybrid metaheuristics algorithms to increase the exploration and exploitation capacity of traditional meta-heuristic algorithms. In this study, a hybrid optimization method that combines the pelican algorithm with the black hole algorithm is presented, which achieves a lower mean squared error (MSE) in comparison to other competing techniques. Data merging through the suggested hybrid metaheuristics algorithm gives superior performance in terms of computing time when compared to both the CV-method and the GCV-method. This work has practical implications for researchers and practitioners who use statistical modeling techniques in their work, especially those dealing with data merging for improved accuracy and efficiency.
Black hole algorithm (BHA) , Pelican optimization algorithm (POA) , semiparametric model , kernel methods , cross-validation , data fusion.
[1] A. Ullah, A. T. K. Wan, H. Wang, X. Zhang, and G. Zou, "A semiparametric generalized ridge estimator and link with model averaging," Econometric Reviews, vol. 36, pp. 370-384, 2015.
[2] M. Roozbeh, M. Arashi, and H. A. Niroumand, "Semiparametric Ridge Regression Approach in Partially Linear Models," Communications in Statistics - Simulation and Computation, vol. 39, pp. 449-460, 2010.
[3] H. Emami, "Local influence for Liu estimators in semiparametric linear models," Statistical Papers, vol. 59, pp. 529-544, 2016.
[4] R. F. Engle, C. W. Granger, J. Rice, and A. Weiss, "Semiparametric estimates of the relation between weather and electricity sales," Journal of the American statistical Association, vol. 81, pp. 310-320, 1986.
[5] E. Goetghebeur and L. Ryan, "Semiparametric regression analysis of interval‐censored data," Biometrics, vol. 56, pp. 1139-1144, 2000.
[6] Y. Qin, S. Zhang, X. Zhu, J. Zhang, and C. Zhang, "Semi-parametric optimization for missing data imputation," Applied Intelligence, vol. 27, pp. 79-88, 2007.
[7] X. Liu, J. Ning, X. He, B. C. Tilley, and R. Li, "Semiparametric regression modeling of the global percentile outcome," Journal of Statistical Planning and Inference, vol. 222, pp. 149-159, 2023.
[8] T. Zhang, "Semiparametric model building for regression models with time-varying parameters," Journal of Econometrics, vol. 187, pp. 189-200, 2015.
[9] E. D. D. Nkou, "Recursive kernel estimator in a semiparametric regression model," Journal of Nonparametric Statistics, pp. 1-27, 2022.
[10] M. Roozbeh, "Shrinkage ridge estimators in semiparametric regression models," Journal of Multivariate Analysis, vol. 136, pp. 56-74, 2015.
[11] O. M. Ismael, O. S. Qasim, and Z. Y. Algamal, "A new adaptive algorithm for v-support vector regression with feature selection using Harris hawks optimization algorithm," in Journal of Physics: Conference Series, 2021, p. 012057.
[12] O. S. Qasim and Z. Y. Algamal, "A gray wolf algorithm for feature and parameter selection of support vector classification," International Journal of Computing Science and Mathematics, vol. 13, pp. 93-102, 2021.
[13] H. Tamiminia, S. Homayouni, H. McNairn, and A. Safari, "A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations," International Journal of Applied Earth Observation and Geoinformation, vol. 58, pp. 201-212, 2017.
[14] Israa Ezzat Salem, Mijwil, M., Alaa Wagih Abdulqader, Marwa M. Ismaeel, Anmar Alkhazraji, & Anas M. Zein Alaabdin. (2022). Introduction to The Data Mining Techniques in Cybersecurity . Mesopotamian Journal of CyberSecurity, 2022, 28–37. https://doi.org/10.58496/MJCS/2022/004
[15] A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, and R. Tavakkoli-Moghaddam, "Red deer algorithm (RDA): a new nature-inspired meta-heuristic," Soft Computing, vol. 24, pp. 14637-14665, 2020.
[16] S. G. Mahmood Al-Kababchee, O. S. Qasim, and Z. Y. Algamal, "Improving penalized regression-based clustering model in big data," Journal of Physics: Conference Series, vol. 1897, 2021.
[17] P. Trojovsky and M. Dehghani, "Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications," Sensors (Basel), vol. 22, Jan 23 2022.
[18] M. Y. Mustafa and Z. Y. Algamal, "Smoothing parameter selection in kernel nonparametric regression using bat optimization algorithm," Journal of Physics: Conference Series, vol. 1897, 2021.
[19] A. Hatamlou, "Black hole: A new heuristic optimization approach for data clustering," Information sciences, vol. 222, pp. 175-184, 2013.
[20] M. Nemati, R. Salimi, and N. Bazrkar, "Black holes algorithm: a swarm algorithm inspired of black holes for optimization problems," IAES International Journal of Artificial Intelligence, vol. 2, p. 143, 2013.
[21] W. Gao, X. Wang, S. Dai, and D. Chen, "Study on stability of high embankment slope based on black hole algorithm," Environmental Earth Sciences, vol. 75, p. 1381, 2016.
[22] F. Q. Kareem and A. M. Abdulazeez, "Ultrasound medical images classification based on deep learning algorithms: a review," Fusion: Practice and Applications, vol. 3, pp. 29-42, 2021.
[23] C. Weng, R. B. Ghazali, S. Mustafa, A. N. Kareem, and B. A. Khalaf, "Weather forecasting for batu pahat using neural network," Fusion: Practice and Applications, vol. 6, pp. 64-70, 2021.
[24] E. Akdeniz, F. Akdeniz, and M. Roozbeh, "A new difference-based weighted mixed Liu estimator in partially linear models," Statistics, vol. 52, pp. 1309-1327, 2018.
[25] T.-H. Dang-Ha, F. M. Bianchi, and R. Olsson, "Local short term electricity load forecasting: Automatic approaches," in 2017 international joint conference on neural networks (ijcnn), 2017, pp. 4267-4274.
