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: Rough Set Theory: A True Landmark in Data Analysis
  name:
    name part: Abraham, Ajith.
    role:
      role term: editor.
  name:
    name part: Falcón, Rafael.
    role:
      role term: editor.
  name:
    name part: Bello, Rafael.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 324 p. online resource.
  abstract: Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.
  table of contents: Theoretical Contributions to Rough Set Theory -- Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces -- Categorical Innovations for Rough Sets -- Granular Structures and Approximations in Rough Sets and Knowledge Spaces -- On Approximation of Classifications, Rough Equalities and Rough Equivalences -- Rough Set Data Mining Activities -- Rough Clustering with Partial Supervision -- A Generic Scheme for Generating Prediction Rules Using Rough Sets -- Rough Web Caching -- Software Defect Classification: A Comparative Study of Rough-Neuro-fuzzy Hybrid Approaches with Linear and Non-linear SVMs -- Rough Hybrid Models to Classification and Attribute Reduction -- Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem -- Nature Inspired Population-Based Heuristics for Rough Set Reduction -- Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis.
  note: edited by Ajith Abraham, Rafael Falcón, Rafael Bello.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer-aided engineering
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Computer Science
  subject:
    topic: Computer-Aided Engineering (CAD, CAE) and Design
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TA345-345.5
  classification: 620.00420285
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 174
  identifier: 9783540899211
  identifier: http://dx.doi.org/10.1007/978-3-540-89921-1
  record info:
    record creation date: 100301
    record change date: 20151204154636.0
    record identifier: 978-3-540-89921-1