Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach

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dc.contributor.authorBazan, Jan. G.
dc.contributor.authorBazan-Socha, Stanislawa
dc.contributor.authorBuregwa-Czuma, Sylwia
dc.contributor.authorPardel, Przemyslaw Wiktor
dc.contributor.authorSkowron, Andrzej
dc.contributor.authorSokolowska, Barbara
dc.date.accessioned2014-11-07T11:52:56Z
dc.date.available2014-11-07T11:52:56Z
dc.date.issued2013
dc.descriptionpl_PL.UTF-8
dc.description.abstractThe 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.pl_PL.UTF-8
dc.description.sponsorshipThis work was supported by the grant N N516 077837 from the Ministry of Science and Higher Education of the Republic of Poland, the Polish National Science Centre (NCN) grant 2011/01/B/ST6/03867 and by the Polish National Centre for Research and Development (NCBiR) grant No. SP/I/1/77065/10 in frame of the the strategic scientific research and experimental development program: ``Interdisciplinary System for Interactive Scientific and Scientific-Technical Information''.pl_PL.UTF-8
dc.identifier.citationPublikacja 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).
dc.identifier.urihttp://repozytorium.ur.edu.pl/handle/item/618
dc.language.isoengpl_PL.UTF-8
dc.publisherSpringer-Verlagpl_PL.UTF-8
dc.subjectrough setpl_PL.UTF-8
dc.subjectconcept approximationpl_PL.UTF-8
dc.subjectcomplex dynamical systempl_PL.UTF-8
dc.subjectontology of conceptspl_PL.UTF-8
dc.subjectbehavioral pattern identificationpl_PL.UTF-8
dc.subjectautomated planningpl_PL.UTF-8
dc.titleClassifiers Based on Data Sets and Domain Knowledge: A Rough Set Approachpl_PL.UTF-8
dc.typebookPartpl_PL.UTF-8
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