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Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications

  • Muhammad Summair Raza
  • Usman Qamar

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Muhammad Summair Raza, Usman Qamar
    Pages 1-25
  3. Muhammad Summair Raza, Usman Qamar
    Pages 27-51
  4. Muhammad Summair Raza, Usman Qamar
    Pages 53-79
  5. Muhammad Summair Raza, Usman Qamar
    Pages 81-107
  6. Muhammad Summair Raza, Usman Qamar
    Pages 109-134
  7. Muhammad Summair Raza, Usman Qamar
    Pages 135-147
  8. Muhammad Summair Raza, Usman Qamar
    Pages 149-158
  9. Muhammad Summair Raza, Usman Qamar
    Pages 159-177
  10. Muhammad Summair Raza, Usman Qamar
    Pages 179-188
  11. Muhammad Summair Raza, Usman Qamar
    Pages 189-227
  12. Muhammad Summair Raza, Usman Qamar
    Pages 229-236

About this book

Introduction

This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms.

The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book.

This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.


Keywords

Feature Selection (FS) Rough Set Theory (RST) Attribute Reduction Dimensionality Reduction RSAR

Authors and affiliations

  • Muhammad Summair Raza
    • 1
  • Usman Qamar
    • 2
  1. 1.Department of Computer and Software Engineering, College of Electrical and Mechanical EngineeringNational University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Department of Computer and Software Engineering, College of Electrical and Mechanical EngineeringNational University of Sciences and Technology (NUST)IslamabadPakistan

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-32-9166-9
  • Copyright Information Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-32-9165-2
  • Online ISBN 978-981-32-9166-9
  • Buy this book on publisher's site