Rough fuzzy hybridization

The volume provides a collection of twenty articles containing new material and describing, in a unified way, the basic concept and characterising features of rough set theory and its integration with fuzzy set theory, for developing an efficient soft computing strategy of machine learning. The articles, written by leading experts all over the world, demonstrates how rough-fuzzy hybridization can be made in various ways to provide flexible information processing capabilities for handling different real life ambiguous decision making problems. Application domain includes, among others, data mining, signal processing, pattern classification, feature/rule generation, knowledge based expert systems, medical information systems and neural computation. Methods of integrating rough-fuzzy hybridization with artificial neural networks for efficient knowledge encoding and network architecture design are described. The volume provides a balanced mixture of both theory and applications with extensive bibliography.