抄録
In most of conventional approaches used for pattern
classification using unsupervised ANN either method of
clusterification is used or the entire feature set is used.
Redundancy in such cases makes the dimensionality of the feature
space too complex to handle. Also early convergence is another
desired factor for training phase of such networks for which
different architectures or learning algorithms are to be tried. A
rough set is one such tool of approximation, which works well
when there is lots of inconsistency in data or even missing data is
there. Hence approaches using rough sets can be used at
preprocessing level, learning level or neuron architectural level.
Thus this paper discusses preprocessing and architectural level
approaches using Rough sets for overall performance
improvement of such pattern classifier for case of character
recognition.
