Keynote Speaker / 特邀报告人

Prof. Witold Pedrycz, IEEE Fellow, University of Alberta, Canada
Professor and Chair
Canada Research Chair

 

 

 

 

 

 

Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering. Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer). He currently serves on the Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.

 

Speech Title: Granular Computing: At the Frontiers of Knowledge Representation and Processing

 

Abstract: In the plethora of rapidly progressing and advanced areas of information technology including software technology, management, and service sciences, it is anticipated that the resulting artifacts of analysis and synthesis take into consideration available data and domain knowledge. They lead to the development of tangible and experimentally legitimized descriptors (concepts), and associations.

Concepts constitute a concise manifestation of data and serve as a backbone of efficient user-centric processing. As being built at the higher level of abstraction than the data themselves, they capture the essence of data and usually emerge in the form of information granules.

We identify three main ways in which concepts are encountered and characterized: (i) numeric, (ii) symbolic, and (iii) granular. Each of these views come with their advantages and limitations as well as become complementary to some extent. The numeric concepts are built by engaging various clustering techniques. The quality of numeric concepts evaluated at the numeric level is described by a reconstruction criterion. The symbolic description of concepts, which is predominant in the realm of Artificial Intelligence (AI) and symbolic computing, can be represented by sequences of labels (integers). In such a way qualitative aspects of data are captured. This facilitates further qualitative analysis of concepts and constructs involving them by reflecting the bird’s-eye view of the data and relationships among them. They come hand in hand with a variety of analyses concerning constructs involving symbols, namely stability, distinguishability, redundancy, and conflict. The granular concepts augment numeric concepts by bringing information granularity into the picture and invoking the principle of justifiable granularity in their construction. 

We elaborate on the general scheme of processing of granular modeling dwelling upon a collection of granular concepts and forming a collection of granular models.


 

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