Rough set approaches to unsupervised neural network based pattern classifier

Abstract

Unsupervised neural network based pattern classification is a widely popular choice for many real time applications. Such applications always face challenges of processing data with lot of consistency, inconsistency, ambiguity or incompleteness. Hence to deal with such challenges a strong approximation tool is always needed. Rough set is one such tool and various approaches based on Rough set, if are applied to pure neural (unsupervised) pattern classifier can yield desired results like faster convergence, feature space reduction and improved classification accuracy. The application of such approaches at respective level of implementation of neural network based pattern classifier for two case studies are discussed here. Whereas more emphasis is given on the preprocessing level based approach used for feature space reduction. © 2010 Springer Science+Business Media B.V.