LAUSR

Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to automatically adjust the models’ parameters. These models were built around two machine learning-based algorithms, an extreme learning machine neural network and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets indicated that optimized algorithms models could better the performance and robustness of the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool to analyze different contexts of data.