Akademik Veri Yönetim Sistemi
2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025, Bursa, Türkiye, 10 - 12 Eylül 2025, (Tam Metin Bildiri)
Support Vector Regression (SVR) is one of the frequently used methods for regression estimation. Kernel selection in SVR is of great importance for the training and estimation of the model. Determining the parameters of the selected kernel is at least as important as the selected kernel. The two parameters of the Radial Basis Function (RBF) kernel are determined stochastically with 5 different current and popular meta-heuristic optimization algorithms and their performances are evaluated on 7 different data sets. The performances of Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Kepler Optimization Algorithm (KOA), Weighted Average Algorithm (WAA) and Hippopotamus Optimization (HO) algorithms in hyper-parameter optimization are compared. Data sets are separated into training and test with k-10 cross-validation. WAA was the model with the highest R 2 value among the models, with values of 0.8521,0.9493, and 0.8392 in 3 of the 7 data sets, respectively. HO was the model with the highest R 2 value among the models, with values of 0.9374, and 0.9375 in 2 of the 7 datasets, respectively. The SVR model, which did not undergo hyper-parameter optimization with the optimization algorithm, was the model with the highest R 2 value among the models, with values of 0. 8 6 6 9 and 0. 5 9 2 8 in 2 of the 7 datasets. Among the algorithms performing hyperparameter optimization, KOA was found to be the fastest algorithm in terms of run time, while HO was found to be the slowest.