A model for heterogeneous neurons and its use in configuring neural networks for classification problems

Abstract

In the classical neuron model inputs are continuous real-valued quantities. However in many important domains from the real world, objects are described by a mixture of continuous and discrete variables and usually containg missing information. A general class of neuron models accepting heterogeneous inputs in the form mixtures of continous and discrete quantities admiting missing data is presented. From these, several particular models can be derived as instances and also different neural architectures can be constructed with them. In particular, hybrid feedforward neural networks composed by layers of heterogeneous and classical neurons are studied here, and a training procedure for them is constructed using genetic algoritmhs. Their possibilities in solving classification and diagnostic problems are illustrated by experiments with data sets from known repositories. The experiments shows that these networks are robust and that they can both learn and classify complex data very effectively and without preprocessing or variable transformations, also in the presence of missing information.