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Automatic feature selection for adaptive resolution classifiers

Classification can be considered as a basic data driven modeling problem, which allows us to define and design more complex modeling systems. The choice of an adequate classification system should take into account the automation degree of the learning procedure, especially if it must be employed as a core inference engine. Fuzzy min-max neural networks are very effective and flexible classification models, since they easily allow the design of constructive learning techniques, such as the ARC/PARC one. In this paper we propose a classification system able to generate automatically a fuzzy min-max classifier. It holds the capability to optimize both the number of neurons in the hidden layer and the set of features used to classify a pattern, without any knowledge about the test set. Its performances are evaluated through a toy problem and two real data benchmarks.