A Graph Theory Approach to Fuzzy Rule Base Simplification

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)

Samenvatting

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.
Originele taal-2Engels
TitelInformation Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
RedacteurenMarie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager
Pagina's387-401
Aantal pagina's15
ISBN van elektronische versie978-3-030-50146-4
DOI's
StatusGepubliceerd - 15 jun 2020
Evenement18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020) - Lisboa, Portugal
Duur: 15 jun 202019 jun 2020
https://ipmu2020.inesc-id.pt/

Publicatie series

NaamCommunications in Computer and Information Science
Volume1237 CCIS
ISSN van geprinte versie1865-0929
ISSN van elektronische versie1865-0937

Congres

Congres18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020)
LandPortugal
StadLisboa
Periode15/06/2019/06/20
Internet adres

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