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Fitting Symmetric Fuzzy Measures for Discrete Sugeno Integration

  • Marek GągolewskiEmail author
  • Simon James
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 642)

Abstract

The Sugeno integral has numerous successful applications, including but not limited to the areas of decision making, preference modeling, and bibliometrics. Despite this, the current state of the development of usable algorithms for numerically fitting the underlying discrete fuzzy measure based on a sample of prototypical values – even in the simplest possible case, i.e., assuming the symmetry of the capacity – is yet to reach a satisfactory level. Thus, the aim of this paper is to present some results and observations concerning this class of data approximation problems.

Keywords

Sugeno integral Aggregation functions Machine learning Regression Approximation 

Notes

Acknowledgments

This study was supported by the National Science Center, Poland, research project 2014/13/D/HS4/01700. Data provided by Scopus.com.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Systems Research Institute, Polish Academy of SciencesWarsawPoland
  2. 2.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  3. 3.School of Information TechnologyDeakin UniversityBurwoodAustralia

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