FUZZ-IEEE 2008 Special Session on "Advances in Granular Computing"

Organizers:

T.Y.Lin
Professor of Computer Science
San Jose State University
San Jose, California 95192-0103, USA
Tel: 408-924-5121, Fax: 408-924-5062
tylin@cs.sjsu.edu

Tzung-Pei Hong
Professor of Computer Science and Information Engineering
National University of Kaohsiung
Der-Chung Road, Nan-Tzu District
Kaohsiung 811, Taiwan
tphong@nuk.edu.tw

Shusaku Tsumoto
Professor of Medical Informatics
Shimane University
School of Medicine
89-1 Enya-cho, Izumo-city |
Shimane 693-8501, Japan
tsumoto@computer.org


Session Description:

Rough set theory is a computational model of approximate reasoning proposed by Z. Pawlak in 1982. The main idea is that human observations may not be able to measure or describe accurately concepts given by the nature. Pawlak used on two approximate measurements or descriptions: One is a set of sufficient conditions, such as a set of manifestations called lower approximation. The other one is a set of necessary conditions, such as a collection of all possible symptoms observed in patients of flu, called upper approximation.

Although rough sets have been studied independently, L.A. Zadeh gave a new insight into this field by observing that rough set theory is a crisp-based theory of granular computing, which is a label proposed by T. Y. Lin (and L. A. Zadeh) to name the notion rooted deeply in fuzzy theory. Zadeh (1979) stated "information granularity . . . to decomposition and partition-- in the theory of automata and system science . . . to bounded uncertainties -- in optimal control. . .". To promote the notion, Lin (and Zadeh) started a special interest group on granular computing (SIGGrC) in BISC (Berkeley Institute of Soft Computing) during his sabbatical leave to Berkeley (1996-97).

Rough sets apply fundamentals of set classification to database analysis (equivalence classes are information granules), which is a crisp-set based granular computing. On the other hand, in granular computing, new theory and methods are proposed involving not only rough sets but also fuzzy sets, probability (e.g., granular probability by G. Klir) multisets, neural networks, belief networks, modal logic, and rule induction method.

This special session gives a special opportunity for researchers on rough sets and granular computing to exchange their ideas.

Paper Submission

Manuscripts should be prepared according to the standard format and page limit of regular papers specified in Fuzzy-IEEE 2008. For submission instructions, please see the WCCI submission page at http://www.wcci2008.org/submission.htm.

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