讲座名称：Data Analytics with Information Granules
讲座时间：2016年1月19日（周二）下午15:00-17:00
讲座地点：北校区图书馆一楼A-108
讲 座 人：Witold Pedrycz 教授
讲座人介绍：
Witold Pedrycz(M’88–SM’90–F’99) received the M.Sc., Ph.D., and D.Sci. degrees from Silesian Universityof Technology, Gliwice, Poland, in 1977,2000, and 2004, respectively.He is a Professor and Canada Research Chair(Computational Intelligence) in the Department ofElectrical and Computer Engineering, University of Alberta, Edmonton, Canada.In 2009, he was elected as a foreign member of the Polish Academy of Sciences. In 2012, he was elected as a fellow ofthe Royal Society of Canada. His main researchdirections involve computational intelligence, fuzzy modeling and granularcomputing, knowledge discovery and data mining, fuzzy control, pattern recognition,knowledge-based neural networks, relational computing, and softwareengineering. He has published numerous papers in this area. He is also anauthor of 14 research monographs covering various aspects of computationalintelligence and software engineering. Prof. Pedrycz has been a member ofnumerous program committees of IEEE conferences in the area of fuzzy setsand neurocomputing. He is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences and IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—SYSTEMS. Currently, he serves as an Associate Editor of IEEE TRANSACTIONS ON FUZZY SYSTEMS and is a Member of a number of editorial boards of other international journals. In2007, he received a prestigious Norbert Wiener award from the IEEE Systems,Man, and Cybernetics Council. He is a recipient of the IEEE Canada ComputerEngineering Medal 2008. In 2009, he has received a Cajastur Prize for Soft Computing from the European Center for Soft Computing for “pioneering andmultifaceted contributions to Granular Computing”.
讲座内容：
The apparent challenges in data analytics inherently associate with large volumes of data, data variability, and a quest for transparency and interpretability of obtained results. We advocate that information granules play a pivotal role in addressing these key challenges. We demonstrate that a framework of Granular Computing along with a diversity of its formal settings offers a badly needed conceptual and algorithmic setting instrumental for data analytics. We elaborate on selected ways in which information granules and their processing address help in coping with abundance of data. A suitable perspective built with the aid of information granules is advantageous in realizing a suitable level of abstraction and forming sound, problem-oriented tradeoffs among precision of results, easiness of their interpretation, value of the results and their stability. All those aspects emphasize importance of actionability and interestingness of the produced findings. Discussed are ways of forming information granules carried out on a basis of abundant data. We show an involvement of efficient granular mechanisms facilitating an inclusion of domain knowledge and making the results of ensuing data analytics user-centric. The development of information granules of higher type and higher order is advocated and their unique role in realizing a hierarchy of processing and coping with a distributed nature of available data is presented. The facet of variability of data is addressed effectively by invoking the mechanisms of transfer learning applied to the adjustment of information granules.
