CISIM 2010


Michał Woźniak, Associate Professor

Biography:
Michał Woźniak
Michal Wozniak is Professor of Computer Science in the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Poland. He received an M.S. degree in Biomedical Engineering in 1992 from the Wroclaw University of Technology, Ph.D. and D.Sc. (habilitation) degrees in Computer Science in 1996 and 2007 respectively from the same university. His research focuses on multiple classifier systems, machine learning, data and web mining, Bayes compound theory, distributed algorithms, computer and networks security and teleinformatics. Prof. Wozniak has published over 140 papers, 2 books and edited 3 books Computer Recognition Systems (Springer). He is editor in chief of International Journal of Computer Networks and Communications, member of editorial board of several international journals including Pattern Analysis and Applications and Journal of Electronic Science and Technology and guest editor of Expert Systems, Neurocomputing, Neural Network World, Information Fusion, Logic Journal of IGPL, and International Journal of Communication Networks and Distributed Systems. He serves on program committees of numerous international conferences. His works have been transitioned into commercial applications. Prof. Wozniak has involved in many research projects related to machine learning, computer networks and telemedicine. Moreover, he has been consulting several commercial projects for the well known Polish companies and public administration. Prof. Wozniak is a member of IEEE (Computational Intelligence Society and Systems Man and Cybernetics Society) and IBS (International Biometric Society).

See detail profile at: http://www.kssk.pwr.wroc.pl/pracownicy/michal.wozniak-en
Keynote Title:
Chosen Problems of Designing Effective Multiple Classifier Systems

Abstract:
We encounter pattern recognition problems on an everyday basis. Therefore, methods of automatic pattern recognition form one of the main trends in Artificial Intelligence. The aim of each such recognition task is to classify a given object of interest by assigning it to some predefined category, on the basis of observing the features of the object. There is much current research into developing even more efficient and accurate recognition algorithms, like neural networks, statistical and symbolic learning to name only a few. Multiple classifier systems (MCSs) are currently the focus of intense research. In this conceptual approach, the main effort is concentrated on combining knowledge of the set of elementary classifiers.
There is a number of important issues while building the aforementioned MCSs. Firstly, how should classifiers be selected such that the decision making quality of the ensemble is superior to that of any individual classifier. This can be considered the problem of classifier synergy. So it seems interesting to select members of a committee with possibly different components. Another important issue is the choice of a collective decision making method. The first group of methods includes algorithms for classifier fusion at the level of their responses The second group of collective decision making methods exploit classifier fusion based on discriminant analysis, the main form of which are the posterior probability estimators, associated with probabilistic models of a given pattern recognition task. Design of new fusion classification models, especially those with a trained fuser block, are currently the focus of intense research.
Proposed lecture presents a brief survey of aforementioned issues connected with MCSs.