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    Wyświetlanie 1-9 z 9
    Tytuł:
    Rough Sets Methods in Feature Reduction and Classification
    Autorzy:
    Świniarski, R. W.
    Powiązania:
    https://bibliotekanauki.pl/articles/908366.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rozpoznawanie obrazów
    redukcja danych
    rough sets
    feature selection
    classification
    Opis:
    The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The paper also contains a description of the algorithm for feature selection and reduction based on the rough sets method proposed jointly with Principal Component Analysis. Finally, the paper presents numerical results of face recognition experiments using the learning vector quantization neural network, with feature selection based on the proposed principal components analysis and rough sets methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 565-582
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough Modeling---a Bottom-up Approach to Model Construction
    Autorzy:
    Loken, T.
    Komorowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908362.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model opisowy
    wybieranie danych
    knowledge discovery
    rough sets
    rough modeling
    descriptive models
    Opis:
    Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen objects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality---it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive and descriptive qualities, in addition to being computationally simple enough to handle large data sets. As rough models are flexible in nature and simple to generate, it is possible to generate a large number of models and search through them for the best model. Initial experiments confirm that the drop in performance of rough models compared to models induced using traditional rough set methods is slight at worst, and the gain in descriptive quality is very large.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 675-690
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Knowledge, Vagueness and Logic
    Autorzy:
    Wybraniec-Skardowska, U.
    Powiązania:
    https://bibliotekanauki.pl/articles/908372.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    zbiór rozmyty
    logika formalna
    vague knowledge
    fuzzy sets
    rough sets
    vague sets
    formal logic
    Opis:
    The aim of the paper is to outline an idea of solving the problem of the vagueness of concepts. The starting point is a definition of the concept of vague knowledge. One of the primary goals is a formal justification of the classical viewpoint on the controversy about the truth and object reference of expressions including vague terms. It is proved that grasping the vagueness in the language aspect is possible through the extension of classical logic to the logic of sentences which may contain vague terms. The theoretical framework of the conception refers to the theory of Pawlak's rough sets and is connected with Zadeh's fuzzy set theory as well as bag (or multiset) theory. In the considerations formal logic means and the concept system of set theory have been used. The paper can be regarded as an outline of the logical theory of vague concepts.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 719-737
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough sets based on Galois connections
    Autorzy:
    Madrid, Nicolás
    Medina, Jesús
    Ramírez-Poussa, Eloísa
    Powiązania:
    https://bibliotekanauki.pl/articles/330499.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rough sets
    Galois connection
    approximation operator
    zbiór przybliżony
    połączenie Galois
    operator aproksymacji
    Opis:
    Rough set theory is an important tool to extract knowledge from relational databases. The original definitions of approximation operators are based on an indiscernibility relation, which is an equivalence one. Lately, different papers have motivated the possibility of considering arbitrary relations. Nevertheless, when those are taken into account, the original definitions given by Pawlak may lose fundamental properties. This paper proposes a possible solution to the arising problems by presenting an alternative definition of approximation operators based on the closure and interior operators obtained from an isotone Galois connection. We prove that the proposed definition satisfies interesting properties and that it also improves object classification tasks.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 2; 299-313
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Minimal Decision Rules Based on the Apriori Algorithm
    Autorzy:
    Fernandez, M. C.
    Menasalvas, E.
    Marban, O.
    Pena, J. M.
    Millan, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/908364.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    baza danych
    algorytmy
    rough sets
    rough dependencies
    association rules
    a priori algorithm
    minimal decision rules
    Opis:
    Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 691-704
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Rough Set-Based Knowledge Discovery Process
    Autorzy:
    Zhong, N.
    Skowron, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/908370.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    baza danych
    baza wiedzy
    system hybrydowy
    rough sets
    KDD process
    hybrid systems
    Opis:
    The knowledge discovery from real-life databases is a multi-phase process consisting of numerous steps, including attribute selection, discretization of real-valued attributes, and rule induction. In the paper, we discuss a rule discovery process that is based on rough set theory. The core of the process is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules from databases with uncertain and incomplete data. The system is based on a combination of Generalization Distribution Table (GDT) and the Rough Set methodologies. In the preprocessing, two modules, i.e. Rough Sets with Heuristics (RSH) and Rough Sets with Boolean Reasoning (RSBR), are used for attribute selection and discretization of real-valued attributes, respectively. We use a slope-collapse database as an example showing how rules can be discovered from a large, real-life database.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 603-619
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Concept Approximations Based on Rough Sets and Similarity Measures
    Autorzy:
    Saquer, J.
    Deogun, J. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/908365.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    mierzenie podobieństwa
    pojęcie aproksymacji
    formal concept analysis
    similarity measures
    rough sets
    concept approximation
    Opis:
    The formal concept analysis gives a mathematical definition of a formal concept. However, in many real-life applications, the problem under investigation cannot be described by formal concepts. Such concepts are called the non-definable concepts (Saquer and Deogun, 2000b). The process of finding formal concepts that best describe non-definable concepts is called the concept approximation. In this paper, we present two different approaches to the concept approximation. The first approach is based on rough set theory while the other is based on a similarity measure. We present algorithms for the two approaches.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 655-674
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Relative Sets and Rough Sets
    Autorzy:
    Mousavi, A.
    Jabedar-Maralani, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/908368.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    teoria mnogości
    analiza danych
    przedstawienie wiedzy
    rough sets
    set theory
    data analysis
    multi-valued logic
    interval sets
    knowledge representation
    Opis:
    In this paper, by defining a pair of classical sets as a relative set, an extension of the classical set algebra which is a counterpart of Belnap's four-valued logic is achieved. Every relative set partitions all objects into four distinct regions corresponding to four truth-values of Belnap's logic. Like truth-values of Belnap's logic, relative sets have two orderings; one is an order of inclusion and the other is an order of knowledge or information. By defining a rough set as a pair of definable sets, an integrated approach to relative sets and rough sets is obtained. With this definition, we are able to define an approximation of a rough set in an approximation space, and so we can obtain sequential approximations of a set, which is a good model of communication among agents.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 637-653
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applications of rough sets in big data analysis: An overview
    Autorzy:
    Pięta, Piotr
    Szmuc, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2055175.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rough sets theory
    big data analysis
    deep learning
    data mining
    teoria zbiorów przybliżonych
    duży zbiór danych
    uczenie głębokie
    eksploracja danych
    Opis:
    Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 659--683
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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