Improving machine learning prediction using strawberry algorithm
Abstract
In this paper, the Support Vector Regression (SVR) model was used, which is defined as an algorithm or a linear model used to predict a specific model. The performance efficiency of the SVR method depends on the selection of its hyperparameters. In this paper, the SVR method was used with the Strawberry Algorithm, which is the proposed algorithm to obtain the best combination of hyperparameters.The Root Mean Squares Error (RMSE) criterion was used to compare the results obtained using the proposed algorithm with some common algorithms, namely, Grid Search, Genetic Algorithm, Particle swarm optimization, and an annealing algorithm (Simulated Annealing algorithm. Three methods of selection were also used in the strawberry algorithm, roulette wheel selection, elite selection, and roulette wheel with the elite selection method together. The performance of the algorithm was tested through experimental and real data. The results showed that the strawberry algorithm was superior to the common algorithms in choosing the best combination of hyperparameters. The results also showed that the method of choosing the roulette wheel is the best method that gave good results compared to other methods on the experimental and applied sides.
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