Particule Swarm Model Selection (PSMS)
H. Jair Escalante, Manuel Montes, L. Enrique Sucar,
Nat. Inst. of Astrophysics Optics and Electronics , Mexico

Neural networks have been proven to be effective learning algorithms since their introduction. These methods have been widely used in many domains, including scientific, medical, and commercial applications with great success. However, selecting the optimal combination of preprocessing methods and hyperparameters for a given learning algorithm is still a challenge. Recently a novel algorithm for supervised learning model selection has been proposed: Particle Swarm Model Selection (PSMS). This algorithm has been proved to be a reliable method for the selection of optimal learning algorithms together with preprocessing methods, as well as for hyperparameter optimization. On this paper we applied PSMS for the selection of the best combination of preprocessing methods and hyperparameters for a fixed neural network on benchmark data sets from a challenging competition: the (IJCNN 2007) agnostic vs prior knowledge challenge. A fair competition which goal is to provide objective comparisons among models that use prior knowledge of the domain and those that not. In this paper we further show that the use of PSMS is useful for model selection when we have no knowledge about the domain we are dealing with. With PSMS we obtained competitive models that are ranked at the top of the official results ranking.