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, usually
containing missing information and uncertainty. In this paper, a general class of neuron mode
heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy)
quantities admitting missing data is presented. From these, several
particular models can be derived as instances and different neural
architectures constructed with them. Such models deal in a natural way
with problems for which information is imprecise or even missing. Their
classification and diagnostic problems are here illustrated by experiments with
data from a real world domain in the field of environmental studies. These
experiments show that such neurons can both learn and classify complex data
very effectively in the presence of uncertain information.
CitationValdes, J., Belanche, Ll., Alquezar, R. "Fuzzy heterogeneous heurons for imprecise classification problems". 1998.
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