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Intelligent Decision Support

Handbook of Applications and Advances of the Rough Sets Theory

  • Roman Słowiński

Part of the Theory and Decision Library book series (TDLD, volume 11)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Applications of the Rough Sets Approach to Intelligent Decision Support

    1. Front Matter
      Pages 1-1
    2. Ryszard Nowicki, Roman Słowiński, Jerzy Stefanowski
      Pages 33-48
    3. Adam J. Szladow, Wojciech P. Ziarko
      Pages 49-60
    4. Krzysztof Słowiński
      Pages 77-93
    5. Maciej Kandulski, Jacek Marciniec, Konstanty Tukałło
      Pages 95-110
    6. Hideo Tanaka, Hisao Ishibuchi, Takeo Shigenaga
      Pages 111-117
    7. Michael Hadjimichael, Anita Wasilewska
      Pages 137-151
    8. Andrzej Reinhard, Urszula Wybraniec-Skardowska, Boguslaw Stawski, Tomasz Weber
      Pages 153-163
    9. Tadeusz Łuba, Janusz Rybnik
      Pages 181-199
  3. Comparison with Related Methodologies

    1. Front Matter
      Pages 201-201
    2. Didier Dubois, Henri Prade
      Pages 203-232
    3. Ewa Krusińska, Ankica Babic, Roman Słowiński, Jerzy Stefanowski
      Pages 251-265
    4. Jacques Teghem, Mohammed Benjelloun
      Pages 267-286
    5. Tsau Y. Lin
      Pages 287-304
    6. Lech T. Polkowski
      Pages 305-311
  4. Further Developments

    1. Front Matter
      Pages 313-313
    2. Maria E. Orlowska, Marian W. Orlowski
      Pages 315-329
    3. Andrzej Skowron, Cecylia Rauszer
      Pages 331-362
    4. Krzysztof Słowiński, Roman Słowiński
      Pages 363-372
    5. Andrzej Lenarcik, Zdzisław Piasta
      Pages 373-389
    6. Dimiter Vakarelov
      Pages 391-399
    7. Zbigniew M. Wójcik, Barbara E. Wójcik
      Pages 401-418
  5. Back Matter
    Pages 457-473

About this book

Introduction

Intelligent decision support is based on human knowledge related to a specific part of a real or abstract world. When the knowledge is gained by experience, it is induced from empirical data. The data structure, called an information system, is a record of objects described by a set of attributes.
Knowledge is understood here as an ability to classify objects. Objects being in the same class are indiscernible by means of attributes and form elementary building blocks (granules, atoms). In particular, the granularity of knowledge causes that some notions cannot be expressed precisely within available knowledge and can be defined only vaguely. In the rough sets theory created by Z. Pawlak each imprecise concept is replaced by a pair of precise concepts called its lower and upper approximation. These approximations are fundamental tools and reasoning about knowledge.
The rough sets philosophy turned out to be a very effective, new tool with many successful real-life applications to its credit.
It is worthwhile stressing that no auxiliary assumptions are needed about data, like probability or membership function values, which is its great advantage.
The present book reveals a wide spectrum of applications of the rough set concept, giving the reader the flavor of, and insight into, the methodology of the newly developed disciplines. Although the book emphasizes applications, comparison with other related methods and further developments receive due attention.

Keywords

Analysis algorithms classification control algorithm fuzzy information system learning set theory sets

Editors and affiliations

  • Roman Słowiński
    • 1
  1. 1.Institute of Computing ScienceTechnical University of PoznańPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-015-7975-9
  • Copyright Information Springer Science+Business Media B.V. 1992
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-4194-4
  • Online ISBN 978-94-015-7975-9
  • Buy this book on publisher's site
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