Chapter 2
Rough-Fuzzy Hybridization and Granular Computing
2.1 Introduction
Soft computing denotes a consortium of methodologies that works synergistically and provides in one form or another flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low cost solutions. The guiding principle is to devise methods of computation that lead to an acceptable solution at low cost by seeking an approximate solution to an intractable problem. Chapter 1 described the relevance of different essential components of the soft computing paradigm such as artificial neural networks, genetic algorithms, fuzzy logic, and rough sets to pattern recognition and data mining problems. Various integrations of these tools to complement each other have also been mentioned.
During the past decade, there have been several attempts to derive hybrid methods by judiciously combining the merits of fuzzy logic and rough sets under the name rough-fuzzy or fuzzy-rough computing. One of the challenges of this hybridization is how to integrate these two tools synergistically to achieve both generic and application-specific merits. This is done in a cooperative, rather than a competitive, manner. The result is a more intelligent and robust system providing a human interpretable, low cost, approximate solution, as compared to traditional ...
