Random Machines: an application to general psychopathological symptoms prediction

Anderson Ara, Federal University of Parana

Co-authors: Mateus Maia, Glasgow University; Alexandre Loch, University of Sao Paulo

Abstract: The improvement of statistical learning models to increase efficiency in solving prediction problems is a goal pursued by the scientific community.  Particularly, the support vector machine model has become one of the most successful algorithms for this task. Despite the strong predictive capacity from the support vector approach, its performance relies on the selection of hyperparameters of the model, such as the kernel function that will be used. In this paper, we proposed a novel method to deal with the kernel function selection called Random Machines. The application to the psychopathological symptoms prediction overlaps traditional machine learning methods.