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Michał Woźniak is a professor of computer science at the Department of Systems and Computer Networks, Wrocław University of Technology, Poland. He received an M.Sc. degree in biomedical engineering from the Wrocław University of Technology in 1992, and Ph.D. and D.Sc. (habilitation) degrees in computer science in 1996 and 2007, respectively, from the same university. His research focuses on machine learning, distributed algorithms, and teleinformatics. Professor Woźniak has published over 200 papers and two books, and edited eight books. He has been involved in several research projects related to the above-mentioned topics and has been a consultant of several commercial projects for well-known Polish companies and public administration. Professor Woźniak is a senior member of the IEEE and a member of the International Biometric Society. This CV is very short and synthetic. For full information please visit website www.kssk.pwr.wroc.pl/wozniak.

Application of combined classifiers to data stream classification

The progress of computer science caused that many institutions collected huge amount of data, which analysis is impossible by human beings. Nowadays simple methods of data analysis are not sufficient for efficient management of an average enterprise, since for smart decisions the knowledge hidden in data is highly required, among them methods of collective decision making called multiple classifier systems are the focus of intense research. Unfortunately the great disadvantage of traditional classification methods is that they "assume" that statistical properties of the discovered concept (which model is predicted) are being unchanged. In real situation we could observe so-called concept drift, which could be caused by changes in the probabilities of classes or/and conditional probability distributions of classes. The potential for considering new training data is an important feature of machine learning methods used in security applications (spam filters or IDS/IPS) or decision support systems for marketing departments, which need to follow the changing client behaviour. Unfortunately, the occurrence of this phenomena dramatically decreases classification accuracy. The talk will focus on combined classifiers (motivations, structure, models applied to data stream classification) and detecting concept change.