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Pancerz K.,University of Management and Administration | Pancerz K.,Rzeszow University of Technology
2013 6th International Conference on Human System Interactions, HSI 2013 | Year: 2013

Approximation of sets is a fundamental notion of rough set theory (RST) proposed by Z. Pawlak. In a classic approach, considered in RST, approximation of sets is defined on the basis of an indiscernibility relation between objects in some universe of discourse. However, approximations of sets become problematic in many cases, especially, if attribute values describing objects are symbolical (e.g., words, terms, linguistic concepts, etc.). In fact, such a situation is natural in human cognition and description of the real world. Different approaches perfecting rough set theory in this area have been proposed in the literature. One of them is based on incorporating ontologies enabling us to add some new, valuable knowledge which can be used in data analysis, rule generation, reasoning, etc. In the paper, we propose to use ontological graphs in determining approximations of sets and show how ontological graphs change a look at them. The presented approach refers to a general trend in computations proposed by L. Zadeh and called 'computing with words'. © 2013 IEEE.


Pancerz K.,University of Management and Administration | Pancerz K.,Rzeszow University of Technology
Advances in Intelligent Systems and Computing | Year: 2013

In the paper, we consider decision rules in simple decision systems over ontological graphs. We assign ontological graphs extending the knowledge about attribute values (i.e., adding some semantic meanings of attribute values) to condition attributes in such systems. Semantics changes the view on properties of decision rules, especially, on the validity of rules in decision systems. Decision rules and related notions are defined analogously to those defined for classic decision systems in rough set theory. © Springer International Publishing Switzerland 2013.


Pancerz K.,University of Management and Administration | Pancerz K.,Rzeszow University of Technology
International Conference on Information and Digital Technologies, IDT 2015 | Year: 2015

In the paper, we introduce the first version of a computer tool called CLAPSS (Classification and Prediction Software System) for solving different classification and prediction problems using, among others, some specialized approaches based mainly on rough set theory. The tool was designed for the Java platform. Selected functionality of CLAPSS is described in the paper. It concerns mining data included in episode information systems, as well as mining data included in information/decision systems over ontological graphs. © 2015 IEEE.


Burda A.,University of Management and Administration | Hippe Z.S.,Rzeszow University of Technology
3rd International Conference on Human System Interaction, HSI'2010 - Conference Proceedings | Year: 2010

A new procedure for combined validation of learning models - developed for specifically uncertain data - is briefly described; it relies on a combination of resubstitution with the modified learn-and-test paradigm, called by us the queue validation. In the initial experiment the elaborated procedure was checked on doubtful (presumably distorted by creative accounting) data, related to small and medium enterprises of the Podkarpackie-region in Poland. Validated in the research learning models were completed in the form of decision trees and sets of production rules. Correctness of both types of models (trees and rules) was estimated basing on the error rate of classification. It was found that false-positive classification errors were significantly larger than false-negative ones; the difference discovered by validation procedure can be probably used as a hint of fraud in the evaluated data. ©2010 IEEE.


Schumann A.,Rzeszow University of Technology | Pancerz K.,Rzeszow University of Technology | Pancerz K.,University of Management and Administration
Fundamenta Informaticae | Year: 2014

Our research is focused on creation of a new object-oriented programming language for Physarum polycephalum computing. Physarum polycephalum is a one-cell organism that can be used for developing a biological architecture of different abstract devices, among others, the digital ones. In the paper, we use an abstract graphical language in the form of Petri nets to describe the Physarum polycephalum behavior. Petri nets are a good formalism to assist designers and support hardware design tools, especially in developing concurrent systems. At the beginning stage considered in this paper, we show how to build Petri net models, and next implement them as Physarum polycephalum machines, of basic logic gates AND, OR, NOT, and simple combinational circuits on the example of the 1-to-2 demultiplexer.


Pancerz K.,University of Management and Administration | Pancerz K.,Rzeszow University of Technology | Lewicki A.,Rzeszow University of Technology
Neurocomputing | Year: 2014

In the paper, we present the idea of encoding symbolic features appearing in simple decision systems over ontological graphs for building classifiers based on Particle Swarm Optimization (PSO) as well as Neural Networks. Simple decision systems over ontological graphs refer to a general trend in computations proposed by Zadeh and called "computing with words". In case of such decision systems, we deal with attribute values, describing objects of interest, which are concepts placed in semantic spaces expressed by means of ontological graphs. Ontological graphs deliver us some additional knowledge which can be useful in classification processes. Symbolic data, in our approach in the form of concepts from ontologies, require special treatment to be used in classifiers based on searching for the numerical mapping functions between the known inputs and the corresponding known outputs. © 2014 Elsevier B.V.


Pancerz K.,University of Management and Administration | Pancerz K.,Rzeszow University of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Approximation of sets is a fundamental notion of rough set theory (RST) proposed by Z. Pawlak. Each rough set can be characterized numerically by the coefficient called the accuracy of approximation. This coefficient determines quantitatively a degree of roughness. Such an approach does not take into consideration semantics of data. In the paper, we show that adding information on semantic relations between decision attribute values in the form of ontological graphs enables us to determine qualitatively the accuracy of approximation. The qualitative assessment of approximation should be treated as some additional characteristic of rough sets. The proposed approach enriches application of rough sets if decision attribute values classifying objects are symbolical (e.g., words, terms, linguistic concepts, etc.). The presented approach refers to a general trend in computations proposed by L. Zadeh and called “computing with words”. © Springer-Verlag Berlin Heidelberg 2015.


Schumann A.,Rzeszow University of Technology | Pancerz K.,Rzeszow University of Technology | Pancerz K.,University of Management and Administration
CEUR Workshop Proceedings | Year: 2013

In the paper, we present foundations of a new object-oriented programming language for Physarum polycephalum computing. Both, theoretical foundations and assumptions for a language specification are considered. Physarum polycephalum is a one-cell organism. In the phase of plasmodium, its behavior can be regarded as a biological substrate that implements the Kolmogorov-Uspensky machine which is the most generalized and nature-oriented version of a mathematical machine. The proposed language will be used for developing programs for Physarum polycephalum by the spatial configuration of stationary nodes (inputs).


Schumann A.,Rzeszow University of Technology | Pancerz K.,Rzeszow University of Technology | Pancerz K.,University of Management and Administration
CEUR Workshop Proceedings | Year: 2014

In the paper, we show that timed transition system models can be used as a high-level model of behavior of Physarum machines. A Physarum machine is a programmable amorphous biological computer experimentally implemented in the vegetative state of Physarum poly-cephalum. Timed transition system models have been used in our new object-oriented programming language for Physarum polycephalum computing.


Omiotek Z.,University of Management and Administration | Burda A.,University of Management and Administration | Wojcik W.,Lublin University of Technology
Expert Systems with Applications | Year: 2013

Methods for classification of ultrasound thyroid images have been presented. These methods allow us to classify examined patients as either sick or healthy. Decision tree induction and a multilayer perceptron neural network have been used to build classification models. Test results showed that the proposed methods can provide a starting point for building a support system in the process of medical diagnosis. Better accuracy of classifiers was achieved for the normalized images. We have also found that, under adopted assumptions, the results obtained for them were statistically significant in contrast to original images. The proposed methods allow us to separate a fairly large group of incorrectly classified cases. According to the authors, this group may contain features of the early stage of Hashimoto's disease. © 2013 Published by Elsevier Ltd.

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