Surrogate-assisted evolutionary multi-objective full model selection
ALEJANDRO ROSALES PEREZ
Classification problems have become a popular task in pattern recognition. This is, perhaps, because they can be used in a number of problems, such as text categorization, handwriting recognition, etc. This has resulted in a large number of methods. Some of theses methods, called pre-processing, aim at preparing the data to be used and others, called learning algorithms, aim at learning a model that maps from the input data into a category. Additionally, most of them have a set of adjustable parameters, called hyper-parameters, that directly impact the performance of the learned models. Hence, when a classification model is constructed, one has to choose among the set of methods and to configure the corresponding hyper-parameters, which can result in a decision with a high number of degrees of freedom. The latter could be a shortcoming when non-expert machine learning users have to face such a problem.
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