The handling of don't care attributes

Abstract

A critical factor that affects the performance of neural network training algorithms and the generalization of trained networks is the training instances. The authors consider the handling of don't care attributes in training instances. Several approaches are discussed and their experimental results are presented. The following approaches are considered: (1) replace don't care attributes with a fixed value; (2) replace don't care attributes with their maximum or minimum encoded values; (3) replace don't care attributes with their maximum and minimum encoded values; and (4) replace don't care attributes with all their possible encoded values.<>