Algorithms Analysis in Adjusting the SVM Parameters: An Approach in the Prediction of Protein Function

Support Vector Machine (SVM) is a supervised learning algorithm widely used in data classification problems. However, the quality of the solution is related to the chosen kernel function, and the adjustment of its parameters. In the present study we compare a genetic algorithm (GA), a particle swarm optimization(PSO), and the grid-search in setting the parameters and C of SVM. After running some experimental tests based on the prediction of protein function, it is concluded that all algorithms are suitable to set the SVM parameters efficiently, yet grid-search runs up to 6 times faster than GA and 30 times faster than PSO.