Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization
I Nyoman Setiawan, Robert Kurniawan, Budi Yuniarto, Rezzy Eko Caraka, Bens Pardamean
Abstract
Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.
Topics & Concepts
Support vector machineComputer scienceKernel (algebra)MetaheuristicBenchmark (surveying)Artificial intelligenceRegressionMachine learningPattern recognition (psychology)Data miningStatisticsMathematicsGeographyCombinatoricsGeodesyData Mining and Machine Learning ApplicationsMetaheuristic Optimization Algorithms ResearchEnergy Load and Power Forecasting