Wyniki wyszukiwania - Biblioteka Nauki

1

Knowledge discovery by application of rough set models

100%

Stepaniuk J.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

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1999

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tom nr 887

3-136

EN

The amount of electronic data avalaible is growing very fast and this explosive growth in databases has generated a need for new techniques and tools that can intelligently and automatically extract implicit, previously unknown, hidden and potentially useful information and knowledge from these data. These tools and techniques are the subject of the field of Knowledge Discovery in Databases. In this paper we discuss selected rough set based solutions to two main knowledge discovery problems, namely the description problem and the classification (predication) problem.

PL

Rozmair i licza dostępnych baz danych wzrasta bardzo szybko. W związku z tym istnieje duże zapotrzebowanie nie tylko na efektywne przechowywanie danych, ale również na automatyczne wydobywanie z danych istotnej, wcześniej nieznanej, a potencjalnie użytecznej wiedzy. Nagląca stała się potrzeba rozwoju nowych technik, które pozwoliłyby na automatyczne wydobywanie wiedzy z takich danych. Opracowanie metod i narzędzi służących temu celowi wchodzi w zakres dziedziny odkrywania wiedzy z baz danych. Celem pracy jest prezentacja wybranych rozwiązań, opartych o teorię zbiorów przybliżonych, dwóch głównych problemów odkrywania wiedzy, mianowicie: opisu i klasyfikacji (przewidywania).

2

Approximation Spaces in Rough–Granular Computing

63%

Skowron A. , Stepaniuk J.

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tom Vol. 100, nr 1/4

141-157

EN

We discuss some generalizations of the approximation space definition introduced in 1994 [24, 25]. These generalizations are motivated by real-life applications. Rough set based strategies for extension of such generalized approximation spaces from samples of objects onto their extensions are discussed. This enables us to present the uniform foundations for inducing approximations of different kinds of granules such as concepts, classifications, or functions. In particular, we emphasize the fundamental role of approximation spaces for inducing diverse kinds of classifiers used in machine learning or data mining.

3

Adaptive Rough Entropy Clustering Algorithms in Image Segmentation

63%

Małyszko D. , Stepaniuk J.

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tom Vol. 98, nr 2/3

199-231

EN

High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information into segmentation process has resulted in development of innovative frameworks such as fuzzy systems, rough systems and recently rough - fuzzy systems. Data analysis based on rough and fuzzy systems is designed to apprehend internal data structure in case of incomplete or uncertain information. Rough entropy framework proposed in [12, 13] has been dedicated for application in clustering systems, especially for image segmentation systems. We extend that framework into eight distinct rough entropy measures and related clustering algorithms. The introduced solutions are capable of adaptive incorporation of the most important factors that contribute to the relation between data objects and makes possible better understanding of the image structure. In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented. At the same time, rough entropy based measures applied in the domain of image segmentation quality evaluation have been compared with standard image segmentation indices. Additionally, rough entropy measures seem to comprehend properly properties validated by different image segmentation quality indices.

4

Algorytm generowania reguł pierwszego rzędu wykorzystujący metody zbiorów przybliżonych

63%

Stepaniuk J. , Góralczuk L.

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2002

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tom Z.1

235-250

PL

W pracy przedstawiono algorytm generowania reguł pierwszego rzędu, tzn. zależności, które w poprzedniku mają koniunkcję formuł atomowych bądź ich negacji a w następniku formułę atomową. Technikę zbiorów przybliżonych wykorzystano w procesie doboru literałów mogących wchodzić w skład przesłanki generowanej reguły. Kryterium doboru opiera się na tym, aby reguła po dołączeniu do jej przesłanki kandydującego literału jak najlepiej rozróżniała przykłady pozytywne i negatywne, które do tej pory nie były rozróżnialne.

EN

The aim of this paper is to introduce and investigate an algorithm for finding first order rules. Rough set theory is used in the process of selecting literals, which may be part of the rule. The criterion of selecting literals reads as follows: only those literals are selected, which adding to the rule makes that the rule discerns the most examples from those, which were yet undiscerned.

5

Rozstrzygnięcie konfliktów między regułami

63%

Kościuk K. , Stepaniuk J.

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2002

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tom Z.1

97-118

PL

W pracy przedstawiono metody klasyfikacji obiektów oparte na teorii zbiorów przybliżonych i sztucznych sieci neuronowych. Omówiono wyniki eksperymentów dotyczących klasyfikatorów zespołowych używających reguł decyzyjnych i sieci neuronowej do rozstrzygania konfliktów między nimi.

EN

In this paper the methods of objects classification based on rough set theory and artificial neural networks are presented. The results of the experiments were discussed in relation to coupled classifier using decision rules and neural network to resolve the conflicts between them.

6

Information Granule Decomposition

63%

Skowron A. , Stepaniuk J.

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tom Vol. 47, nr 3,4

337-350

EN

Information sources provide us with granules of information that must be transformed, analyzed and built into structures that support problem solving. One of the main goals of information granule calculi is to develop algorithmic methods for construction of complex information granules from elementary ones by means of available operations and inclusion (closeness) measures. These constructed complex granules represent a form of information fusion. Such granules should satisfy some constraints like quality criteria or/and degrees of granule inclusion in (closeness to) a given information granule. Information granule decomposition methods are important components of those methods. We discuss some information granule decomposition methods.

7

Computational complexity of multimodal logics based on rough sets

63%

Demri S. , Stepaniuk J.

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tom Vol. 44, Nr 4

373-396

EN

We characterize the computational complexity of a family of approximation multimodal logics in which interdependent modal connectives are part of the language. Those logics have been designed to reason in presence of incomplete information in the sense of rough set theory. More precisely, we show that all the logics have a PSPACE-complete satisfiability problem and we define a family of tolerance approximation multimodal logics whose satisfiability is EXPTIME-complete. This illustrates that the PSPACE upper bound for this kind of multimodal logics is a very special feature of such logics. The PSPACE upper bounds are established by adequately designing Ladner-style tableaux-based procedures whereas the EXPTIME lower bound is established by reduction from the global satisfiability problem for the standard modal logic B.

8

Optimization in Discovery of Compound Granules

51%

Jankowski A. , Peters J. F. , Skowron A. , Stepaniuk J.

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2008

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tom Vol. 85, nr 1-4

249-265

EN

The problem considered in this paper is the evaluation of perception as a means of optimizing various tasks. The solution to this problem hearkens back to early research on rough set theory and approximation. For example, in 1982, Ewa Orowska observed that approximation spaces serve as a formal counterpart of perception. In this paper, the evaluation of perception is at the level of approximation spaces. The quality of an approximation space relative to a given approximated set of objects is a function of the description length of an approximation of the set of objects and the approximation quality of this set. In granular computing (GC), the focus is on discovering granules satisfying selected criteria. These criteria take inspiration from the minimal description length (MDL) principle proposed by Jorma Rissanen in 1983. In this paper, the role of approximation spaces in modeling compound granules satisfying such criteria is discussed. For example, in terms of approximation itself, this paper introduces an approach to function approximation in the context of a reinterpretation of the rough integral originally proposed by Zdzisaw Pawlak in 1993. We also discuss some other examples of compound granule discovery problems that are related to compound granules representing process models and models of interaction between processes or approximation of trajectories of processes. All such granules should be discovered from data and domain knowledge. The contribution of this article is a proposed solution approach to evaluating perception that provides a basis for optimizing various tasks related to discovery of compound granules representing rough integrals, process models, their interaction, or approximation of trajectories of discovered models of processes.

9

Rough Sets Based LEM2 Rules Generation Supported by FPGA

51%

Kopczyński M. , Grześ T. , Stepaniuk J.

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tom Vol. 148, nr 1/2

107--122

EN

In this paper we propose a combination of capabilities of the FPGA based device and PC computer for rough sets based data processing resulting in generating decision rules. Presented architecture has been tested on the exemplary datasets. Obtained results confirm the significant acceleration of the computation time using hardware supporting rough set operations in comparison to software implementation.

10

Modelling Complex Patterns by Information Systems

51%

Stepaniuk J. , Bazan J. G. , Skowron A.

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tom 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.