Kolegium Nauk Przyrodniczych / College of Natural Sciences

URI dla tego Zbioruhttp://repozytorium.ur.edu.pl/handle/item/14

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Aktualnie wyświetlane 1 - 9 z 9

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.
Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data
(Humboldt University, 2014) Bazan, Jan G.; Szpyrka, Marcin; Szczur, Adam; Dydo, Łukasz; Wojtowicz, Hubert
A new method of constructing classifiers from huge volume of temporal data is proposed in the paper. The novelty of introduced method lies in a multi-stage approach to constructing hierarchical classifiers that combines process mining, feature extraction based on temporal patterns and constructing classifiers based on a decision tree. Such an approach seems to be practical when dealing with huge volume of temporal data. As a proof of concept a system has been constructed for packet-based network traffic anomaly detection, where anomalies are represented by spatio-temporal complex concepts and called by behavioral patterns. Hierarchical classifiers constructed with the new approach turned out to be better than ”flat” classifiers based directly on captured network traffic data.
Extracting of temporal patterns from data for hierarchical classifiers construction
(IEEE, 2015) Szpyrka, M.; Szczur, A.; Bazan, Jan G.; Dydo, Ł.
A method of automatic extracting of temporal patterns from learning data for constructing hierarchical behavioral patterns based classifiers is considered in the paper. The presented approach can be used to complete the knowledge provided by experts or to discover the knowledge automatically if no expert knowledge is accessible. Formal description of temporal patterns is provided and an algorithm for automatic patterns extraction and evaluation is described. A system for packet-based network traffic anomaly detection is used to illustrate the considered ideas.
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.
Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring.
(IEEE Xplore, 2012) Bazan-Socha, Stanisława; Bazan, Jan G.; Buregwa-Czuma, Sylwia; Pardel, Przemysław W.; Sokolowska, Barbara
The purpose of this study was to evaluate the usefulness of classification methods in recognizing cardiovascular pathology. Based on clinical and electrocardiographic (ECG) Holter data we propose the method for predicting coronary stenosis demanding revascularization in patients with diagnosis of stable coronary heart disease. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzisław 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. We present a new modification of tree building method which emphasizes the discernibility of objects belonging to decision classes indicated by human experts. Presented method may be used to assess the need for revascularization and in special circumstances, to confirm or reject the diagnosis of coronary artery disease. The paper includes results of experiments that have been performed on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland.
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.
Rough Set Based Reasoning About Changes
(IOS Press, 2012) Skowron, Andrzej; Stepaniuk, Jarosław; Jankowski, Andrzej; Bazan, Jan G.; Swiniarski, Ryszard
We consider several issues related to reasoning about changes in systems interacting with the environment by sensors. In particular, we discuss challenging problems of reasoning about changes in hierarchical modeling and approximation of transition functions or trajectories. This paper can also be treated as a step toward developing rough calculus.

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Przeglądanie Kolegium Nauk Przyrodniczych / College of Natural Sciences według Autor "Bazan, Jan G."