Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

Preface

Soft computing is a collection of methodologies that work synergistically, not competitively, that, in one form or another, reflects its guiding principle: exploits the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth to achieve tractability, robustness, low cost solution, and close resemblance with human-like decision making. It provides a flexible information processing capability for representation and evaluation of various real-life, ambiguous and uncertain situations and therefore results in the foundation for the conception and design of high machine intelligence quotient systems. At this juncture, the principal constituents of soft computing are fuzzy sets, neurocomputing, genetic algorithms, probabilistic reasoning, and rough sets.

One of the challenges of basic soft computing research is how to integrate its different constituting tools synergistically to achieve both generic and application-specific merits. Application-specific merits point to the advantages of integrated systems not achievable using the constituting tools singly.

Rough set theory, which is considered to be a newer soft computing tool compared with others, deals with uncertainty, vagueness, and incompleteness arising from the indiscernibility of objects in the universe. The main goal of rough set theoretic analysis is to synthesize or construct approximations, in terms of upper and lower bounds of concepts, properties, or relations from the acquired data. The key ...