Skip to main content
  • Book
  • © 2019

Rough Set–Based Classification Systems

Authors:

(view affiliations)
  • Allows the reader to successfully work with sets of indistinguishable values and missing values

  • Develops decision-making systems in two configurations: iterative and collective

  • Written by respected experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 802)

Buying options

eBook
USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-03895-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 149.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
  2. Introduction

    • Robert K. Nowicki
    Pages 1-6
  3. Rough Set Theory Fundamentals

    • Robert K. Nowicki
    Pages 7-16
  4. Rough Fuzzy Classification Systems

    • Robert K. Nowicki
    Pages 17-70
  5. Fuzzy Rough Classification Systems

    • Robert K. Nowicki
    Pages 71-93
  6. Rough Neural Network Classifier

    • Robert K. Nowicki
    Pages 95-132
  7. Rough Nearest Neighbour Classifier

    • Robert K. Nowicki
    Pages 133-159
  8. Ensembles of Rough Set–Based Classifiers

    • Robert K. Nowicki
    Pages 161-184
  9. Final Remarks

    • Robert K. Nowicki
    Pages 185-188

About this book

This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. 

The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.

Keywords

  • Rough Sets Theory
  • Computational Intelligence
  • Decision Making
  • Rough Neural Networks
  • Fuzzy Rough Classifiers
  • Classifiiers

Authors and Affiliations

  • Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Robert K. Nowicki

Bibliographic Information

Buying options

eBook
USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-03895-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 149.99
Price excludes VAT (USA)