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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. Source


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. Source


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. Source


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. Source


Pancerz K.,Rzeszow University of Technology | Pancerz K.,University of Management and Administration | Schumann A.,Rzeszow University of Technology
International Journal of General Systems | Year: 2015

In this paper, we consider transition system models of behaviour of Physarum machines in terms of rough set theory. A Physarum machine, a biological computing device implemented in the plasmodium of Physarum polycephalum (true slime mould), is a natural transition system. In the behaviour of Physarum machines, one can notice some ambiguity in Physarum motions that influences exact anticipation of states of machines in time. To model this ambiguity, we propose to use rough set models created over transition systems. Rough sets are an appropriate tool to deal with rough (ambiguous, imprecise) concepts in the universe of discourse. © 2015 Taylor & Francis. Source

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