PSMS for Neural Networks on the IJCNN 2007 Agnostic vs Prior Knowledge Challenge

Abstract

Artificial 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 data set is still a challenge. Recently a method for supervised learning model selection has been proposed: Particle Swarm Model Selection (PSMS). PSMS is a reliable method for the selection of optimal learning algorithms together with preprocessing methods, as well as for hyperparameter optimization. In this paper we applied PSMS for the selection of the (pseudo) optimal 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 forum for the evaluation of methods for model selection and data representation discovery. 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 high in the official results of the challenge.