A Graph Theory Approach to Fuzzy Rule Base Simplification

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Fuzzy inference systems (FIS) gained popularity and found application in several fields of science over the last years, because they are more transparent and interpretable than other common (black-box) machine learning approaches. However, transparency is not automatically achieved when FIS are estimated from data, thus researchers are actively investigating methods to design interpretable FIS. Following this line of research, we propose a new approach for FIS simplification which leverages graph theory to identify and remove similar fuzzy sets from rule bases. We test our methodology on two data sets to show how this approach can be used to simplify the rule base without sacrificing accuracy.
Original languageEnglish
Title of host publicationInformation Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
EditorsMarie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager
Pages387-401
Number of pages15
ISBN (Electronic)978-3-030-50146-4
DOIs
Publication statusPublished - 15 Jun 2020
Event18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020) - Lisboa, Portugal
Duration: 15 Jun 202019 Jun 2020
https://ipmu2020.inesc-id.pt/

Publication series

NameCommunications in Computer and Information Science
Volume1237 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020)
CountryPortugal
CityLisboa
Period15/06/2019/06/20
Internet address

Keywords

  • Data-driven modeling
  • Fuzzy logic
  • Graph theory
  • Open-source software
  • Python
  • Takagi–Sugeno fuzzy model

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