Skip to main content
SpringerLink
Log in
Book cover

Aggregation and Fusion of Imperfect Information

  • Book
  • © 1998

Overview

Editors:
  1. Bernadette Bouchon-Meunier

    1. Centre National de la Recherche Scientifique, LIP6 — Laboratoire d’Informatique de Paris 6, Université Pierre et Marie Curie, Paris Cedex 05, France

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • A state-of-the-art of the main methodologies to manage imperfect information, as well as examples of their utilizations

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 12)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (14 chapters)

  1. Front Matter

    Pages I-VIII
    Download chapter PDF

  2. Aggregation Operators for Fusion under Fuzziness

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. On Robust Aggregation Procedures

      • Sergei Ovchinnikov
      Pages 3-10

    3. Triangular Norm-Based Aggregation of Evidence Under Fuzziness

      • Radko Mesiar, Magda Komorníková
      Pages 11-35

    4. Aggregation Functions Defined by t-Norms and t-Conorms

      • János Fodor, Tomasa Calvo
      Pages 36-48

  3. Aggregation in Decision Making and Control

    1. Front Matter

      Pages 49-49
      Download chapter PDF

    2. Fuzzy Integral as a Flexible and Interpretable Tool of Aggregation

      • Michel Grabisch
      Pages 51-72

    3. Using Priorities in Aggregation Connectives

      • Antoine Kelman, Ronald R. Yager
      Pages 73-97

    4. Aggregation Operators for Fuzzy Rationality Measures

      • Vincenzo Cutello, Javier Montero
      Pages 98-105

    5. Aggregation in Decision Making with Belief Structures

      • Maria Teresa Lamata
      Pages 106-117

    6. Multistage Fuzzy Control with a Soft Aggregation of Stage Scores

      • Janusz Kacprzyk
      Pages 118-137

  4. Fusion of Complementary Information

    1. Front Matter

      Pages 139-139
      Download chapter PDF

    2. From Semantic to Syntactic Approaches to Information Combination in Possibilistic Logic

      • Salem Benferhat, Didier Dubois, Henri Prade
      Pages 141-161

    3. Aggregation of Imprecise Probabilities

      • Serafín Moral, José del Sagrado
      Pages 162-188

    4. Fusion of Image Information under Imprecision

      • Isabelle Bloch, Henri Maître
      Pages 189-213

    5. Fuzzy Linguistic Methods for the Aggregation of Complementary Sensor Information

      • G. Mauris, E. Benoit, L. Foulloy
      Pages 214-230

    6. Uncertain Data Aggregation in Classification and Tracking Processes

      • Alain Appriou
      Pages 231-260

    7. A Generalized Fuzzy Clustering Model Based on Aggregation Operators and its Applications

      • Mika Sato, Yoshiharu Sato
      Pages 261-278
Back to top

Keywords

About this book

This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and possibility theory. The latter is more developed because it allows to manage both imprecise and uncertain knowledge. Applications to decision-making, image processing, control and classification are described.

Editors and Affiliations

  • Centre National de la Recherche Scientifique, LIP6 — Laboratoire d’Informatique de Paris 6, Université Pierre et Marie Curie, Paris Cedex 05, France

    Bernadette Bouchon-Meunier

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation