rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
  name:
    name part: Raza, Muhammad Summair.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Qamar, Usman.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 194 p. 75 illus. online resource.
  abstract: This book provides a comprehensive introduction to Rough Set-based feature selection. It enables the reader to systematically study all topics of Rough Set Theory (RST) including the preliminaries, advanced concepts and feature selection using RST. In addition, 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. Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, is an area in constant development. Focusing on the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis. Feature selection is one of the important applications of RST, and helps us select the features that provide us with the largest amount of useful information. The book offers a valuable reference guide for all students, researchers, and developers working in the areas of feature selection, knowledge discovery and reasoning with uncertainty, especially those involved in RST and granular computing.
  table of contents: Introduction to Feature Selection -- Background -- Rough Set Theory -- Advance Concepts in RST -- Rough Set Based Feature Selection Techniques -- Unsupervised Feature Selection using RST -- Critical Analysis of Feature Selection Algorithms -- RST Source Code.
  note: by Muhammad Summair Raza, Usman Qamar.
  subject:
    topic: Computer science
  subject:
    topic: Numerical analysis
  subject:
    topic: Database management
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Information Systems Applications (incl. Internet)
  subject:
    topic: Database Management
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Numeric Computing
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811049651
  identifier: http://dx.doi.org/10.1007/978-981-10-4965-1
  record info:
    record creation date: 170628
    record change date: 20170628141452.0
    record identifier: 978-981-10-4965-1