| Lecturer Profile |
Witold Pedrycz 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. He also holds an appointment of special professorship in the School of Computer Science, University of Nottingham, UK. 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. He has received several awards such as Killam Prize in 2013. 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 and Co-Editor-in Chief of several international journals or conferences. |
| Lecture Abstract |
Prediction models and their efficient evaluation arise as an important and timely direction of fundamental and applied research. However, there are no ideal models. It is impossible to envision a situation where any model can deliver an ideal fit to experimental data and needless to say that the quality of any model is of paramount importance to any application. To offer a sound and realistic evaluation of the quality of constructed models, it is legitimate and intuitively appealing to admit that the prediction results come in a certain non-numeric way. The talk focus on the concepts of predictive models with prediction results being realized in the form of information granules. It introduces a comprehensive algorithmic setting as well as a detailed quantification of quality of information granules. Besides, some well-known constructs of statistically-guided prediction in linear regression are briefly reviewed. The generic idea of prediction intervals is generalized to embrace information granules of prediction including constructs. The talk also introduces a concept of a granular parameter space and a granular output space yielding a granular nature of prediction. |
