Pattern classification for incomplete data

The problem of pattern classification for inputs with missing values is considered. A general fuzzy min-max (GFMM) neural network utilising hyperbox fuzzy sets as a representation of data cluster prototypes is used. It is shown how a classification decisions can be carried out on a subspace of high dimensional input data. No substitution scheme for missing values is utilised. The result is a classification procedure that reduces a number of viable class alternatives on the basis of available information rather than attempting to produce one winning class without supporting evidence. A number of simulation results for well known data sets are provided to illustrate the properties and performance of the proposed approach.