In this paper we investigate the potential benefits and
limitations of various data editing procedures when
constructing neuro-fuzzy classifiers based on hyperbox
fuzzy sets. There are two major aspects of data editing
which we are attempting to exploit: a) removal of outliers
and noisy data; and b) reduction of training data size. We
show that successful training data editing can result in
constructing simpler classifiers (i.e. a classifier with a
smaller number and larger hyperboxes) with better
generalisation performance. However we also indicate
the potential dangers of overediting which can lead to
dropping the whole regions of a class and constructing
too simple classifiers not able to capture the class
boundaries with high enough accuracy. A more flexible
approach than the existing data editing techniques based
on estimating probabilities used to decide whether a
point should be removed from the training set has been
proposed. An analysis and graphical interpretations are
given for the synthetic, non-trivial, 2-dimensional
classification problems
