Similarity-based Heterogeneous Neural Networks

This research introduces a general class of functions serving as generalized neuron models to be used in artificial neural networks. They are cast in the common framework of computing a similarity function, a flexible definition of a neuron as a pattern recognizer. The similarity endows the model with a clear conceptual view and leads naturally to handle heterogeneous information, in the form of mixtures of continuous numbers (crisp or fuzzy), linguistic information and discrete quantities (ordinal, nominal and finite sets). Missing data are also explicitly considered. The absence of coding schemes and the precise computation attributed to the neurons makes the networks highly interpretable. The resulting heterogeneous neural networks are trained by means of a special-purpose genetic algorithm. The cooperative integration of different soft computing techniques (neural networks, evolutionary algorithms and fuzzy sets) makes these networks capable of learning from non-trivial data sets with a remarkable effectiveness, comparable or superior to that of classical models. This claim is demonstrated by a set of experiments on benchmarking realworld data sets.