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Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models

  • Emran Saleh
  • Aida Valls
  • Antonio Moreno
  • Pedro Romero-Aroca
  • Vicenç Torra
  • Humberto Bustince
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)

Abstract

Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno \(\lambda \)-measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature.

Keywords

Fuzzy measures Aggregation functions Choquet integral Sugeno integral Fuzzy rule-based systems Diabetic retinopathy 

Notes

Acknowledgements

This work is supported by the URV grant 2017PFR-URV-B2-60, and by the Spanish research projects no: PI12/01535 and PI15/01150 for (Instituto de Salud Carlos III and FEDER funds). Mr. Saleh has a Pre-doctoral grant (FI 2017) provided by the Catalan government and an Erasmus+ travel grant by URV. Prof. Bustince acknowledges the support of Spanish project TIN2016-77356-P.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Emran Saleh
    • 1
  • Aida Valls
    • 1
  • Antonio Moreno
    • 1
  • Pedro Romero-Aroca
    • 2
  • Vicenç Torra
    • 3
  • Humberto Bustince
    • 4
  1. 1.Departament d’Enginyeria Informàtica i MatemàtiquesUniversitat Rovira i VirgiliTarragonaSpain
  2. 2.Ophthalmic ServiceUniversity Hospital Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i VirgiliReusSpain
  3. 3.School of InformaticsUniversity of SkövdeSkövdeSweden
  4. 4.Departamento de Automàtica y ComputaciónUniversidad Pública de Navarra, Institute of Smart CitiesPamplonaSpain

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