Hierarchical Classifiers for Complex Spatio-temporal Concepts

Obrazek miniatury
Bazan, Jan
Tytuł czasopisma
Tytuł tomu
The aim of the paper is to present rough set methods of constructing hierarchical classifiers for approximation of complex concepts. Classifiers are constructed on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. Information systems, decision tables and decision rules are basic tools for modeling and constructing such classifiers. The general methodology presented here is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for (un)structured 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. We describe the results of computer experiments performed on real-life data sets from a vehicular traffic simulator and on medical data concerning the infant respiratory failure.
Jest to praca habilitacyjna dr. hab. Jana G. Bazana, prof. UR. Publikacja opublikowana jako: Bazan, J.: Hierarchical Classifiers for Complex Spatio-temporal Concepts, In Transactions on Rough Sets IX, Lecture Notes in Computer Science 5390, 474-750, Berlin, Heidelberg: Springer-Verlag (2008). The original publication is available at www.sprigerlink.com (Oryginalna publikacja jest dostępna na stronie www.sprigerlink.com).
Słowa kluczowe
rough set , concept approximation , complex dynamical system , ontology of concepts , behavioral pattern identification , automated planning