Rough sets as a front end of neural-networks texture classifiers

Abstract

The paper describes an application of rough sets method to feature selection and reduction as a front end of neural-network-based texture images recognition. The methods applied include singular-value decomposition (SVD) for feature extraction, principal components analysis (PCA) for feature projection and reduction, and rough sets methods for feature selection and reduction. For texture classification the feedforward backpropagation neural networks were applied. The numerical experiments show the ability of rough sets to select reduced set of pattern's features (minimizing the pattern size), while providing better generalization of neural-network texture classifiers.