Przeglądanie według Temat "concept approximation"

Aktualnie wyświetlane 1 - 4 z 4

A classifier based on a decision tree with verifying cuts
(Humboldt University, 2014) Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Dydo, Łukasz; Rząsa, Wojciech; Skowron, Andrzej
This article introduces a new method of a decision tree construction. Such decision tree is constructed with the usage of additional cuts that are used for a veri cation of cuts in tree nodes during the classi cation of objects. The presented approach allows the use of additional knowledge contained in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments that have been performed on data obtained from biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its outcomes with the results of classi cation using a local discretization decision tree, well known from literature. The results of comparison of the two approaches show that making decisions is more adequate through the employment of several attributes simultaneously. Our new method allowed us to achieve better quality of classi cation then the existing method.
A Domain Knowledge as A Tool For Improving Classifiers
(IOS Press, 2013) Bazan, Jan G.; Buregwa-Czuma, Sylwia; Jankowski, Andrzej
This paper investigates the approaches to an improvement of classifiers quality through the application of a domain knowledge. The expertise may be utilizable on several levels of decision algorithms such as: feature extraction, feature selection, a definition of~temporal patterns used in an approximation of the concepts, especially of the complex spatio-temporal ones, an assignment of an object to the concept and a measurement of the objects similarity. The domain knowledge incorporation results then in the reduction of the size of searched spaces. The work constitutes an overview of classifier building methods efficiently utilizing the expertise, worked out latterly by Professor Andrzej Skowron research group. The methods using domain knowledge intended to enhance the quality of classic classifiers, to identify the behavioral patterns and for automatic planning are discussed. Finally it answers a question whether the methods satisfy the hopes vested in them and indicates the directions for future development.
Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach
(Springer-Verlag, 2013) Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Pardel, Przemyslaw Wiktor; Skowron, Andrzej; Sokolowska, Barbara
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.
Hierarchical Classifiers for Complex Spatio-temporal Concepts
(Springer-Verlag, 2008) Bazan, Jan G.
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.