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1
Content available remote The Method for Describing Changes in the Perception of Stenosis in Blood Vessels Caused by an Additional Drug
Buregwa-Czuma S., Bazan J. G., Bazan-Socha S., Rewerska B., Zareba L., Pardel P., Dydo L.
Fundamenta Informaticae
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2016
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Vol. 147, nr 2/3
193--207
EN
The decision making depends on the perception of the world and the proper identification of objects. The perception can be modified by various factors, that alter a way of perceiving the object even though the object is not changed (e.g., in the perception of a medical condition, such factors can be drugs or diet). The purpose of this research is to study how the disturbing factors can influence the perception. The idea was to introduce the description of the rules of these changes. We propose a method for evaluating the effect of additional therapy in patients with coronary heart disease based on the tree of the impact. The leaves of the tree provide crossdecision rules of perception changes which could be suggested as a solution to the problem of predicting changes in perception. The problems considered in this paper are associated with the design of classifiers which allow the perception of the object in the context of information related to the decision attribute.
2
Content available remote Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data
Bazan J. G., Szczur A., Dydo L., Wojtowicz H., Szpyrka M.
Fundamenta Informaticae
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2016
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Vol. 143, nr 1/2
19--34
EN
A new method of constructing classifiers from huge volume of temporal data is proposed in the paper. The novelty of introduced method finds expression in a multi-stage approach to build 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 for packet-based network traffic anomaly detection was constructed, 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.
3
Content available remote A Classifier Based on a Decision Tree with Verifying Cuts
Bazan J. G., Buregwa-Czuma S., Rzasa W., Bazan-Socha S., Dydo L., Skowron A.
Fundamenta Informaticae
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2016
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Vol. 143, nr 1/2
1--18
EN
This article introduces a new method of a decision tree construction. Such construction is performed using additional cuts applied for a verification of the cuts' quality in tree nodes during the classification of objects. The presented approach allows us to exploit the additional knowledge represented in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments performed on data sets from a biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, well known from literature. Our new method out performs the existing method, which is also confirmed by statistical tests.
4
Content available remote A Domain Knowledge as a Tool For Improving Classifiers
Bazan J.G., Buregwa-Czuma S., Jankowski A. W.
Fundamenta Informaticae
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2013
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Vol. 127, nr 1-4
495--511
EN
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.
5
Content available remote Rough Set Based Reasoning About Changes
Skowron A., Stepaniuk J., Jankowski A., Bazan J.G., Swiniarski R.
Fundamenta Informaticae
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2012
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Vol. 119, nr 3/4
421-437
EN
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.
6
Content available remote Case-based Planning of Treatment of Infants with Respiratory Failure
Góra G., Bazan J.G., Kruczek P., Bazan-Socha S., Skowron A., Pietrzyk J.J.
Fundamenta Informaticae
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2008
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Vol. 85, nr 1-4
155-172
EN
We discuss medical treatment planning in the context of case-based planning, where plans (of treatment) are treated as complex decisions. A plan for a particular case is constructed from known plans for similar training examples. In order to evaluate and improve the prediction quality of complex decisions, we use a method for approximation of similarity measure between plans. The method makes it possible to transform the acquired domain knowledge about similarities of plans, expressed by medical experts in natural language, to a low level language understandable by the system. To accomplish this task, we developed a method for approximation of the ontology of concepts expressed by medical experts. We present two applications of the ontology approximation, namely, for approximation of similarity between patient histories and for approximation of compatibility of patient histories with planned therapies. Next, we use these concept approximations to define two measures on which are based two methods for (plan) therapy prediction. The article includes results of experiments with these methods performed on medical data obtained from Neonatal Intensive Care Unit, First Department of Pediatrics, Polish-American Institute of Pediatrics, Collegium Medicum, Jagiellonian University, Kraków, Poland. The experiments are pertained to the identification of infants' death risk caused by respiratory failure.
7
Content available remote Rough Set Approach to Behavioral Pattern Identification
Bazan J.G., Bazan-Socha S., Pietrzyk J.J., Kruczek P., Skowron A.
Fundamenta Informaticae
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2007
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Vol. 75, nr 1-4
27-47
EN
The problem considered is how to model perception and identify behavioral patterns of objects changing over time in complex dynamical systems. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzisaw Pawlak during the early 1980s provides the foundation for the construction of classifiers, relative to what are known as temporal pattern tables. Temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to identify behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning about concepts embedded in such an ontology. We also present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. The article includes results of experiments that have been performed on data from a vehicular traffic simulator and on medical data obtained from Neonatal Intensive Care Unit in the Department of Pediatrics, Collegium Medicum, Jagiellonian University. The contribution of this article is the introduction of a network of classifiers that make it possible to identify the behavioral patterns of objects that change over time.
8
Content available remote Behavioral Pattern Identification Through Rough Set Modeling
Bazan J.G.
Fundamenta Informaticae
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2006
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Vol. 72, nr 1-3
37-50
EN
This paper introduces an approach to behavioral pattern identification as a part of a study of temporal patterns in complex dynamical systems. Rough set theory introduced by Zdzisaw Pawlak during the early 1980s provides the foundation for the construction of classifiers relative to what are known as temporal pattern tables. It is quite remarkable that temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces what are known as behavior graphs. Temporal concepts approximated by approximate reasoning schemes become nodes in behavioral graphs. In addition, we discuss some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to recognize behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning about concepts embedded in such an ontology. We also present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. The article includes results of experiments that have been performed on data from a vehicular traffic simulator useful in the identification of behavioral patterns by drivers.
9
Content available remote Spatio-Temporal Approximate Reasoning over Complex Objects
Synak P., Bazan J.G., Skowron A., Peters J.F.
Fundamenta Informaticae
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2005
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Vol. 67, nr 1-3
249-269
EN
We discuss the problems of spatio-temporal reasoning in the context of hierarchical information maps and approximate reasoning networks (AR networks). Hierarchical information maps are used for representations of domain knowledge about objects, their parts, and their dynamical changes. AR networks are patterns constructed over sensory measurements and they are discovered from hierarchical information maps and experimental data. They make it possible to approximate domain knowledge, i.e., complex spatio-temporal concepts and reasonings represented in hierarchical information maps. Experiments with classifiers based on AR schemes using a road traffic simulator are also briefly presented.
10
Content available remote Modelling Complex Patterns by Information Systems
Stepaniuk J., Bazan J.G., Skowron A.
Fundamenta Informaticae
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2005
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Vol. 67, nr 1-3
203--217
EN
We outline an approach to hierarchical modelling of complex patterns that is based on operations of sums with constraints on information systems. We show that such operations can be treated as a universal tool in hierarchical modelling of complex patterns.
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