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Interpretability Issues in Fuzzy Modeling

  • Jorge Casillas
  • Oscar Cordón
  • Francisco Herrera
  • Luis Magdalena

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 128)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Overview

    1. Front Matter
      Pages 1-1
    2. Jorge Casillas, Oscar Cordón, Francisco Herrera, Luis Magdalena
      Pages 3-22
  3. Improving the Interpretability with Flexible Rule Structures

  4. Complexity Reduction in Linguistic Fuzzy Models

    1. Front Matter
      Pages 77-77
    2. Oscar Cordón, María José Del Jesus, Francisco Herrera, Luis Magdalena, Pedro Villar
      Pages 79-99
    3. Jairo Espinosa, Joos Vandewalle
      Pages 100-124
    4. Pierre-Yves Glorennec
      Pages 125-147
    5. Domonkos Tikk, Tamás D. Gedeon, Kok Wai Wong
      Pages 176-192
    6. Vincent Vanhoucke, Rosaria Silipo
      Pages 193-217
  5. Complexity Reduction in Precise Fuzzy Models

    1. Front Matter
      Pages 219-219
    2. Janos Abonyi, Hans Roubos, Robert Babuska, Ferenc Szeifert
      Pages 221-248
    3. Magne Setnes
      Pages 278-302
    4. Thomas Sudkamp, Aaron Knapp, Jon Knapp
      Pages 303-324
    5. Yeung Yam, Chi Tin Yang, Péter Baranyi
      Pages 325-352
  6. Interpretability Constraints in TSK Fuzzy Rule-Based Systems

  7. Assessments on the Interpretability Loss

  8. Interpretation of Black-Box Models as Fuzzy Rule-Based Models

About this book

Introduction

Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. Among others, it has been applied to knowledge discovery, automatic classification, long-term prediction, or medical and engineering analysis. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy. This approach usually sets aside the interpretability of the obtained models. However, we should remember the initial philosophy of fuzzy sets theory directed to serve the bridge between the human understanding and the machine processing. In this challenge, the ability of fuzzy models to express the behavior of the real system in a comprehensible manner acquires a great importance. This book collects the works of a group of experts in the field that advocate the interpretability improvements as a mechanism to obtain well balanced fuzzy models.

Keywords

automatic classification classification complexity computer-aided design (CAD) fuzzy fuzzy logic fuzzy modeling fuzzy set fuzzy sets group knowledge discovery learning model modeling philosophy

Editors and affiliations

  • Jorge Casillas
    • 1
  • Oscar Cordón
    • 1
  • Francisco Herrera
    • 1
  • Luis Magdalena
    • 2
  1. 1.Dpto. Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería InformáticaUniversidad de GranadaGranadaSpain
  2. 2.Dpto. Matemáticas Aplicadas a las Tecnologías de la Información, Escuela Técnica Superior de Ingenieros de TelecomunicaciónUniversidad Politécnica de MadridMadridSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-37057-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-05702-1
  • Online ISBN 978-3-540-37057-4
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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