Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach
Bazan, Jan. G.
Pardel, Przemyslaw Wiktor
The problem considered is how to construct classifiers for approximation of complex concepts on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. The approach presented in this chapter to solving this problem is based on the rough set theory methods. Rough set theory introduced by Zdzisław Pawlak during the early 1980s provides the foundation for the construction of classifiers. This approach is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for complex objects, to identify the behavioral patterns of complex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio-temporal concepts requiring approximation. The chapter includes results of experiments that have been performed on data from a vehicular traffic simulator and the recent results of experiments that have been performed on medical data sets obtained from Second Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland. Moreover, we also describe the results of experiments that have been performed on medical data obtained from Neonatal Intensive Care Unit in the Department of Pediatrics, Jagiellonian University Medical College, Krakow, Poland.
rough set , concept approximation , complex dynamical system , ontology of concepts , behavioral pattern identification , automated planning
Publikacja opublikowana jako: Bazan, J., G., Bazan-Socha, S., Buregwa-Czuma, S., Pardel, P., Skowron, A., Sokolowska, B.: Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach, In: Skowron, A., Suraj, Z. (Eds.), Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, Vol. 43, Springer-Verlag, Berlin Heidelberg, 2013, pp. 93-136. Oryginalna publikacja jest dostępna na stronie www.sprigerlink.com (The original publication is available at www.sprigerlink.com).