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: Accuracy Improvements in Linguistic Fuzzy Modeling
  name:
    name part: Casillas, Jorge.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Cordón, O.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Herrera Triguero, Francisco.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Magdalena, Luis.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2003
    edition: 1st ed. 2003.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 385 p. 64 illus. online resource.
  abstract: Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is the capability to express the real system behavior in a comprehensible way, and accuracy, which is the capability to faithfully represent the real system. In this framework, one of the most important areas is linguistic fuzzy modeling, where the legibility of the obtained model is the main objective. This task is usually developed by means of linguistic (Mamdani) fuzzy rule-based systems. An active research area is oriented towards the use of new techniques and structures to extend the classical, rigid linguistic fuzzy modeling with the main aim of increasing its precision degree. Traditionally, this accuracy improvement has been carried out without considering the corresponding interpretability loss. Currently, new trends have been proposed trying to preserve the linguistic fuzzy model description power during the optimization process. Written by leading experts in the field, this volume collects some representative researcher that pursue this approach.
  table of contents: Overview -- Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview -- Accuracy Improvements Constrained by Interpretability Criteria -- COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy -- Constrained optimization of genetic fuzzy systems -- Trade-off between the Number of Fuzzy Rules and Their Classification Performance -- Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms -- Fuzzy CoCo: Balancing Accuracy and Interpretability of Fuzzy Models by Means of Coevolution -- On the Achievement of Both Accurate and Interpretable Fuzzy Systems Using Data-Driven Design Processes -- Extending the Modeling Process to Improve the Accuracy -- Linguistic Hedges and Fuzzy Rule Based Systems -- Automatic Construction of Fuzzy Rule-Based Systems: A trade-off between complexity and accuracy maintaining interpretability -- Using Individually Tested Rules for the Data-based Generation of Interpretable Rule Bases with High Accuracy -- Extending the Model Structure to Improve the Accuracy -- A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms -- An Iterative Learning Methodology to Design Hierarchical Systems of Linguistic Rules for Linguistic Modeling -- Learning Default Fuzzy Rules with General and Punctual Exceptions -- Integration of Fuzzy Knowledge -- Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?.
  note: edited by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena.
  subject:
    topic: Business mathematics
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational complexity
  subject:
    topic: Economic theory
  subject:
    topic: Business Mathematics
  subject:
    topic: Mathematical and Computational Engineering
  subject:
    topic: Natural Language Processing (NLP)
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Complexity
  subject:
    topic: Economic Theory/Quantitative Economics/Mathematical Methods
  classification: HF5691-5716
  classification: 330.0151
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Fuzziness and Soft Computing, 129
  identifier: 9783540370581
  identifier: https://doi.org/10.1007/978-3-540-37058-1
  record info:
    record creation date: 130909
    record change date: 20191022021952.0
    record identifier: 978-3-540-37058-1