Przeglądanie według Temat "discretization"

Aktualnie wyświetlane 1 - 3 z 3

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.
Prediction of coronary arteriosclerosis in stable coronary heart disease
(Springer-Verlag, 2012) Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Pardel, Przemysław W.; Sokolowska, Barbara
The aim of the study was to assess the usefulness of classification methods in recognizing cardiovascular pathology. From the medical point of view the study involves prediction of coronary arteriosclerosis presence in patient with stable angina using clinical data and electrocardiogram (ECG) Holter monitoring records. On the grounds of these findings the need for coronary interventions is determined. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzislaw Pawlak during the early 1980s provides the foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on the basis of the local discretization method. The paper includes results of experiments that have been performed on medical data obtained from II Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland.