Information Granulation and Pattern Recognition

Abstract

We discuss information granulation applications in pattern recognition. The chapter consists of two parts. In the first part, we present applications of rough set methods for feature selection in pattern recognition. We emphasize the role of different forms of reducts that are the basic constructs of the rough set approach in feature selection. In the overview of methods for feature selection, we discuss feature selection criteria based on the rough set approach and the relationships between them and other existing criteria. Our algorithm for feature selection used in the application reported is based on an application of the rough set method to the result of principal component analysis used for feature projection and reduction. Finally, the first part presents numerical results of face recognition experiments using a neural network, with feature selection based on proposed principal component analysis and rough set methods. The second part consists of an outline of an approach to pattern recognition with the application of background knowledge specified in natural language. The approach is based on constructing approximations of reasoning schemes. Such approximations are called approximate reasoning schemes and rough neural networks.