Treffer: A Graph Theory Approach to Fuzzy Rule Base Simplification

Title:
A Graph Theory Approach to Fuzzy Rule Base Simplification
Authors:
Lesot M.-J.,Vieira S.,Reformat M.Z.,Carvalho J.P.,Wilbik A.,Bouchon-Meunier B.,Yager R.R., Fuchs, C, Spolaor, S, Nobile, M, Kaymak, U, Fuchs C., Spolaor S., Nobile M. S., Kaymak U.
Publisher Information:
Springer 2020
Document Type:
E-Ressource Electronic Resource
Index Terms:
Data-driven modeling, Fuzzy logic, Graph theory, Open-source software, Python, Takagi–Sugeno fuzzy model, info:eu-repo/semantics/conferenceObject
URL:
http://hdl.handle.net/10281/298366
Availability:
Open access content. Open access content
Note:
English
Other Numbers:
ITBAO oai:boa.unimib.it:10281/298366
10.1007/978-3-030-50146-4_29
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85086254585
1311397932
Contributing Source:
BICOCCA OPEN ARCH
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1311397932
Database:
OAIster

Weitere Informationen

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.